
DeepSeek vs. ChatGPT: Differences and Use Cases
In recent months, the advent of chatbot services that seamlessly converse with humans has piqued the interest of the general public and academic researchers alike. No longer is natural language processing research confined to academic journals or conferences — chatbot services have painted the search engine market red, sparking interest in similar solutions. These chatbots are the first memorable products of highly talented scientists making solutions to incredibly hard natural language tasks freely available for the masses. To paraphrase a well-known saying, we are not so much using technology, as technology is using us. For the average person with little training in the field, these chatbots provide a tantalizing gateway to quickly initiate into the possibilities of natural language processing solutions — producing code, automatic summaries of work, and more in the easy colloquial language of natural language.
There is now an abundance of commercial natural language processing services: some focused on specific tasks, others providing easier access to complex models. On one hand side, plug-and-play solutions that provide quick access to very challenging tasks have been available for some time — such as search engines, automatic summarization, and code generation. On the other hand, there are platforms that allow a variety of transfer learning pipelines, making it easy to reproduce models used for specific tasks, like document-question answering, document-based dialogue, visual question answering, and named entity recognition, among many others. The introduction of broader applications of text generation have paved the way for accessible and user-friendly interfaces for text generation.
Overview of DeepSeek
Artificial Intelligence (AI) is permeating the modern world at an unprecedented rate. With the proliferation of Intellectual Property (IP) activity over the last 100 years, IP professionals have utilized databases and document management tools to trumpet efficiency and maintain service levels. That was until the advent of generative AI models that have disrupted traditional workflow and clearly redefined AI-assisted solutions for the future by making them remotely accessible and lowering the entry barriers to AI for small to mid-sized organizations. DeepSeek is a leading example of a solution that has unmapped the creative potential of generative AI for all IP professionals.
DeepSeek is a Large Language Model (LLM) called NAI-19B specialized in neural architecture and natural language processing trained on 19B parameters about neural architectures and is specifically tuned for patent and scientific literature tasks. DeepSeek offers a simple, fast API for advanced patent and scientific literature tasks. DeepSeek is the first feature rich generalist tool for five use cases important to all IP organizations: Patent and Scientific Literature Prior Art Search, Idea Generation, Technology Monitoring, Particle Patent Message Filtering, and Patent Text Analysis. The production release was featured as the Product of the Day on the day of announcement, April 4th, 2023.
At the back end, DeepSeek has strong capabilities. DeepSeek is based on the open weight LLMs LLaMA, Pythia, and NAI-19B specialized in neural architecture and natural language processing trained on 19B parameters about neural architectures and is specifically tuned for patent and scientific literature tasks. DeepSeek offers a simple, fast API for advanced patent and scientific literature tasks.
Overview of ChatGPT
ChatGPT is a cloud-based generative AI chatbot that specializes in dialogue. It provides users with essay-type written responses to prompts and open-ended questions in a conversational manner. Making use of the latest Generative Pre-trained Transformer Language Model, ChatGPT has become widely recognized for its ability to create text that appears to be written by a human. In 2023, a new version was released for Plus subscribers, offering them enhanced features such as multimodal capabilities, along with a higher word limit for prompts and responses.
The name ChatGPT comes from the “Chat” function that the language model is able to perform due to its fine-tuning, reinforcement learning and several other enhancements that it has received. Launched on November 30, 2022, ChatGPT has gathered much media attention and received both praise and criticism for its conversational abilities. It has been used by the public to complete homework and write essays, articles, poetry, business pitches, and computer code. ChatGPT has the capability to simulate a personality and output text in different voices or styles, within the reasonable limits of its prompt and response length constraints.
Core Technologies
Generative AI technologies gained attention for their captivating capabilities, but few users understand how they really work. In this essay, we show how two tools—DeepSeek, a search technology, and ChatGPT, a generative AI technology—work differently under the hood. At first glance, the DeepSeek and ChatGPT interfaces seem very similar: both allow the user to enter a natural language query and receive a textual answer. However, while ChatGPT generates an answer based on its internal model, which is based on an extensive set of more or less relevant documents, DeepSeek retrieves information from a user-defined set of domain-specific documents using advanced search techniques that allow a user to query a set of private documents that could not typically be asked about via a generic tool like ChatGPT.
Because DeepSeek is based primarily on search technology, the answers it produces are not “made up”, and are more accurate. It can incorporate a large set of documents, which means it can retrieve, combine, and summarize information from the whole collection. This is especially useful when the topic is more complex than a few sentences or paragraphs, as is often the case. With ChatGPT, long or complex answers are created by the “filling-in-the-blank” method, where some or all text in the answer is not based on an actual document, but is filled in by the model, which is less reliable than retrieving a fact-based answer. In this section, we will discuss how the technology behind the two different tools differs, and how this difference leads to varied capabilities.
DeepSeek Technology
DeepSeek is a custom AI engine for document searching and for finding organization knowledge. Because there are huge amounts of manuals, procedures, and training documents in companies, it is very hard for employees to get their answer or what they are looking for, due to inefficient keyword searching engines and cooperation tools.
DeepSeek is a result of RnD effort by the DeepTech Lab, where teams work for building our own customized AI engines for specific purposes. In this paper, we present DeepSeek, an AI engine specifically built for document searching, annotation, and knowledge recommendation.
Explore valuable information using Knowledge Harvesting from your own files and any others. Tired of typing in keywords and searching through huge lists of webpages? With DeepSeek you can ask questions; get proofread answers; follow a thread to get more details; and get summaries from large documents to help you understand quickly. Your own packed with manuals, procedures, technical documents, brochures, presentations and many more! Store your own company knowledgebase to have all your information handy across your whole organization. From one location, get answers to the questions your employees ask the most. Human resources, finance, procurement, marketing, IT or any other areas.
DeepSeek takes care of the need to provide multi-language frontend to allow employees to ask their questions in whatever language they prefer. Select from a huge variety of languages and dialects, DeepSeek will take care of understanding what you mean. DeepSeek is fitted with the best natural language processing models you would find, topped up by our own research and development effort. Trying to find a detailed specific answer to a complex question? Just ask. DeepSeek handles queries submitted in natural language; if you can understand it, DeepSeek can understand it too!
ChatGPT Technology
ChatGPT is a chatbot developed by OpenAI. It’s that AI assistant you may have recently encountered on websites, applications, or even on the guidelines. Your user has input a prompt requesting information to help guide them when traveling. ChatGPT has not yet been particularly specialized. As such, its knowledge is general. But ChatGPT was made very publicly available in 2022 and has had billions of interactions to improve that knowledge. Certainly, the more typical use cases would include text summarization, text generation, FAQ answering tasks, and discussion.
ChatGPT operates using what is known as an autoregressive model built on GPT-3 and GPT-3.5 models, both of which use transformer architecture. Like BERT, it also employs a decoder-only architecture. This architecture is composed of a stack of blocks along with various other layers for input embedding, output projection, and layer normalization. Each block is further composed of some more sub-components: stacked masked multi-head self-attention layers and a feed-forward neural network. It uses the same self-attention mechanism which links tokens in the input sequence by assigned relevance scores, allowing computations to change depending on the given input.
Key Differences
While both DeepSeek and ChatGPT serve the need for instantaneous information access, the context in which they make their technical choices confirms the direction procedure followed by each team. DeepSeek chose a retriever model to pull search results, while ChatGPT trains its model on previously indexed data. The first strategy involves going to the internet and indexing it on-demand. The second consists in pushing data to the model through the model training process so that it understands and encodes all the information contained within the indexed data. Because of this fundamental difference in architecture, DeepSeek and ChatGPT differ significantly in functionality. With additionally superior capabilities for both model types.
Functionality
DeepSeek addresses located or punctual queries. That is, user input questions that require an instant one-step answer or a short list of short answers. For example, Who is the current president of the United States? A typical domain knowledge store. It is also possible to expand a little and ask what are the phone numbers of the Apple company at the Office of Communications in Israel? A specific user query that involves a nested resource. Or, what is the difference between DeepSeek and ChatGPT? A question that anchors a user interaction into a two-way conversational search. ChatGPT enables complete and complex tasks without additional means or resources. Users can also ask more broad and general questions. The quality of the answer may not be satisfactory after the first attempt. Exploring and asking clarifications about the answer may not work either. But the option exists. In terms of external resource usage, a request to ChatGPT can use either an external API, a software routine or a human operator.
Functionality
DeepSeek and ChatGPT are both AI technologies that leverage Machine Learning and Natural Language Processing algorithms. However, they apply their interface in different ways to solve a number of tasks. ChatGPT is a chatbot, focusing on Human-Computer Interaction. This makes it ideal for common use cases of chatbots, such as providing clear and detailed instructions, generating user-focused content, and developing useful software libraries. Furthermore, ChatGPT relies on fast access to all of its knowledge that makes the querying of information from many different topics very efficient.
While ChatGPT serves a general purpose, DeepSeek is specialized in doing fast and context-aware query answering. For instance, if the user is interested in a niche topic, DeepSeek can index documents related to that topic and answer questions specifically about that topic quickly with high accuracy. Furthermore, since DeepSeek incorporates semi-structured content in the form of documents from local directories, it can provide detailed answers to specific data querying with a particular data structure. This makes DeepSeek suitable for a wide range of professional tasks, such as querying market research documents for business intelligence, transparent reports for automated code reviews, and white papers for high-quality software development. Since DeepSeek relies on documents that contain high quality human-provided information, resulting in profound human understanding of the desired topic, it provides knowledge with a depth that chatbots can often only hint at.
User Interface
DeepSeek offers a simple, text-based user interface, allowing users to enter their questions in a search box format. The results are neatly organized in three sections: context, summary, and question. In contrast, ChatGPT uses a multi-modal user interface that supports both text entry and voice queries in visual and audio formats, making it more convenient and versatile for varied types of interactions. Additionally, ChatGPT supports voice interactions, making it a more accessible option for users desiring scientific exchanges.
ChatGPT’s multi-modal user interface certainly stands out, though DeepSeek’s focused interface serves its purpose: helping users process long technical documents rapidly. Instead of needing to read through the entire document and make sense of it, users need only fed their queries to get comprehensive answers, saving precious time. While ChatGPT is intended for interactive dialogue-based use based on the extensive prior text chunks submitted by users, the simplicity of DeepSeek makes obvious that it is designed for answering specific, pre-defined questions about the contents of academic publications. Neither interface is necessarily better than the other; the main difference comes down to user preference.
Performance Metrics
Performance metrics help potential users gauge the ability, reliability, and consistency of a model with their data. The system’s performance usually drives the user to use or not the system to provide results for tasks. The differences in the models motivate different benchmarked tasks, so we focus on common tasks based on what is possible in practice.
As a language model, ChatGPT hasn’t been released as a model and is closed-source. However, a chat fine-tuned model of GPT-3.5 is publicly available. DeepSeek is open-source and was released publicly for local use. Following the research on large models which has released user models, we found that most output performance and reliability come from the tunings instead of the model architecture. So users are concerned about output reliability and output quality.
The first quantitative conclusion you can have is that larger models are slower. But in DeepSeek you will have a better balance if you want speed, since of the 4 released variants, if you take one of the bigger ones like 22B you will have a quite faster model than the smaller variants. The second conclusion is that larger models have a similarity in perplexity, with no huge outliers in the results. In fact, the largest model has the lowest perplexity, so regarding perplexity the better model would be the larger variant.
Use Cases for DeepSeek
The specialty of DeepSeek is answering questions without providing conclusions about the current state of the world. Thus its most direct use cases involve retrieving data from sources that have been prepared without AI assistance. Particularly, these data include specific information found in spreadsheets, data tables, and structured data collections. The feasibility of data retrieval using a specific semantic search depends on the design of the mappings between questions and the data, and on the data retrieval stringency. For example, it could be as easy as posing the question “What are the names of the programmatic advertising firms in Macedonia”, but the data are only partially similar: there are features ensuring that a retrieved firm is about the programmatic advertising, for example, its Description or Product_Description attributes differ from empty and contain synonymous phrases; the value of the country location representing a Macedonia country must match the answer request, that is, the answer must contain the “value” attribute. In this example, the choice of mapping is very restrictive.
Business Intelligence
Financial and related business documents of large organizations have much in common. Thus, such documents entity segments can be quickly located and read. These entity segments can be arranged in a table, with several rows describing an entity and the columns describing an attribute of the entity for every reporting date or period; creating such a table allows for easy financial data comparison between firms. Currently, the firm comparison must be done manually or with a high accuracy rate using AI-powered automated tools. A semantic search with DeepSeek tool can significantly facilitate this painstaking business analysis process, saving the analyst and accountant firms a considerable amount of time. It will also improve the quality of decisions related to firm performance evaluation.
Data Retrieval
Data Retrieval DeepSeek was originally built to search HTML documents and PDF files. Both are common formats for unstructured text data, where a mixture of structured, semi-structured, or unstructured data is used, among others. Such a mixture may contain document metadata in appendix files, introductions in the first few paragraphs of reports, clause topics in the head tags of HTML files, or acronyms in parentheses close to term definitions. People go through digital libraries and datasets matching these descriptions all the time searching for information that is useful for business processes, research papers, and personal life alike. Those processes are extremely easy with DeepSeek, exclusively focusing on single documents or extensive datasets. The retrieval is fast, and results can be reported in a tabular way. DeepSeek does not promise the user the knowledge that is already in the document being searched. Instead, the returned results tell the user where in the document that knowledge can be found. Initially, DeepSeek created a use case of faceted filtering for time series in large document repositories with hundreds of thousands of files between 5MB and 200MB in size-annual reports published in the SEC database. It also has direct applications in several document datasets matching the one described above. Data is organized and direct retrieval is crucial at bibliographic databases, research engines, data catalogs, knowledge discovery portals, and publications at data sharing sites. Besides being a good initial foundation for text analytics, DeepSeek can be used as a search engine and document-centric data filtering application as well. Some cases are data cataloging and searching at scientific repositories, data services, and focus-sharing sites; toolkits or frameworks for data filtration from intranets and corporate databases; academic and life prospect cataloging or searching tools; electronic learning content searching and filtering; and document repository word filtering and searching tools for organizational management and contact processes.
Business Intelligence
Business intelligence is a field that analyzes structured, well-defined information. Business intelligence is defined as “an umbrella term that refers to a variety of tools, applications, and methodologies that enable the collection, analysis, and presentation of business data.” Like a search engine pulls data from the world wide web and presents data in links or snippets, a business intelligence tool retrieves data based on queries and business needs, presenting it either on dashboards, digital documents, or specific business reports. In short, business intelligence tools allow data users, especially in big companies, to analyze their internal data and compare it with benchmarks or other data sources.
Internally, companies usually keep a lot of structured data: for example, accounting or CRM data. However, analyzing it manually can be time-consuming and require a lot of resources. Although companies can also hire IT specialists to analyze the data with specific programming languages, business intelligence tools allow all business people to consult the data instead of restricting usage to classes of data specialists. Also, while corporate databases can contain data for short periods of time, business intelligence tools allow companies to analyze their historical data and draw insights about trends over time. Some business intelligence tools also connect to other sources of data, such as web analytics data, social network data, or advertising platform data.
Research Applications
DeepSeek’s special focus on the academic and research domains provides capabilities like no other tool for scholarly research. For instance, on a recent search for task mining, one cannot expect a research assistant to always return the full history of specific research tasks correctly. It is only with the combination of DeepSeek and other tools that one can expect to obtain such information. Queries like “summarize this research area based on the seed papers in the response” or “summarize the research area based on the documents’ titles, patterns of citations, and their citation relationships”, or “provide short descriptions of each of the seed papers in the response” are important and can be conveniently posed to other tools. Research stories like those in DeepSeek Research Stories rely on DeepSeek’s multi-level citations, co-tasking, and fusion between heterogeneous documents and insights on multiple relationships (including citations and co-citations). For a pure research area definition problem, including DeepSeek outputs would be useful to assist chatbots further. A chatbot answerigner can also be prompt-directed to take into account specific sources, relationships, prompts to perform summarizations or description extraction correctly. For instance, adding prompts like “take into account all the previous responses, and recursively provide summaries at 5 and 3 levels of detail separately” would help.
Use Cases for ChatGPT
Since its launch, ChatGPT has quickly become an attractive tool for various industries. It is remarkably popular among both organizations and individuals. The tool is relatively inexpensive, easily accessible to anyone with a device that allows web browsing, and requires no training to start using. Overall, ChatGPT is one of the best AI chatbots for generating human-like text. Individuals can utilize its capabilities for various tasks, and companies can implement ChatGPT for a multitude of purposes. The following are some of the most popular and useful ways to use ChatGPT.
Customer Support Chatbots have been used by corporations for years to augment customer service and try to alleviate some of the bottlenecks caused by long wait times and fill employee gaps for minor inquiries or tasks. In many instances, companies assign human customer service agents to chat with customers via live chat on their website. While this does work to some extent, often customers have issues that can be solved in just a few short text exchanges, so utilizing a tool like ChatGPT can help streamline this experience. ChatGPT would allow users to immediately solve the following types of issues: resetting passwords, looking up their account information and balances, clarifying certain company policies, or answering questions about a product or service a company offers.
Content Creation All industries, especially the marketing and publishing sectors, require distinctive, but also effective, content to distribute to the public. ChatGPT helps users brainstorm creative ideas for blog posts, video scripts, or even catchy email subject lines. This can help make the creative process a little easier. Additionally, if you need a first draft on a certain topic, ChatGPT is perfect for that. After inputting your desired topic for the draft, ChatGPT can generate something resembling a rough draft in seconds. Once the text is generated, all the user has to do is read through it, edit inaccuracies or errors, and polish it up before publishing.
Customer Support
Use Cases for ChatGPT While ChatGPT is a very flexible tool, and in theory could be used for a wide variety of tasks, it is often better suited for certain tasks because of the underlying technology. There are certainly technical differences that can help determine the best intended use of both DeepSeek and ChatGPT. Understanding these differences can help businesses identify which use cases they should apply DeepSeek to, and which to apply ChatGPT to.
Why You Wouldn’t Use ChatGPT Unable to reference or pull back company-specific information, ChatGPT-generated results are inherently limited to the information within its model. Customers looking for company-specific or the latest information wouldn’t want to directly use ChatGPT. Since queries can often lack contextual information, this too makes responses and follow-up questions limited.
Customer Support Customer Support is one of the most common use cases for ChatGPT. Not only are companies interested in automating these services, but also customers benefit from the fast-paced and efficient nature of ChatGPT answers. Businesses question the ability of ChatGPT and other such AI tools in producing accurate responses and information.
With companies and businesses growing, so are their customer bases which creates an influx of messages. Automating these services and processes can go a long way in decreasing costs and keeping customers satisfied. ChatGPT offers easy integration and is one of the more popular LLMs to implement for these workflows. With users asking repetitive and common questions such as “Where is my order?” or “What’s the store’s timing?” at large volumes, automating these conversations can both improve speed and efficiency.
Because ChatGPT has no knowledge of who you are or what your company is, all responses and information are generic in nature. Companies are unable to directly direct users to a specific FAQ because ChatGPT can’t search through one.
Content Creation
Among the myriad of use cases for ChatGPT, content generation is undoubtedly the most popular, most visible, and most widely used. Both individuals and teams frequently leverage the model’s generative capabilities to develop presentations, long- and short-form text, website copies, informative write-ups, articles, and more. With the introduction of ChatGPT plugins, the model has become even more powerful as you can now use the web-accessible version to find up-to-date information, retrieve real-time data, and even access specific APIs to perform more complex tasks. ChatGPT is certainly being used as an intelligent co-writer.
Be it HTML-based website copies or YAML-formatted configs, ChatGPT can generate code in various languages for many use cases. Need some code? Just ask ChatGPT. ChatGPT is especially useful when you want to prototype or iterate quickly. Educators are using ChatGPT to help students learn concepts better. Simply prompt ChatGPT to explain something to you as an expert in a particular field would to a layperson. Creating question papers for students has never been this easy. Just ask ChatGPT to generate questions based on a topic, and it will spit out a document with questions. You can also decide how many questions you want and at how high a difficulty level. Tell it to be an educator with many years of experience and ask it to assess the questions it has generated.
One of the sectors benefiting from ChatGPT is code education. ChatGPT is widely used in classrooms to assist students when they are learning to code and working on challenging tasks. Students apparently use ChatGPT to debug their homework and help them out when they are stuck.
Personal Assistance
Both ChatGPT and DeepSeek can function well as personal assistants for users. They rely on a knowledge base made up of a large volume of documents to help respond to user requests. The quality of responses will depend on the number of relevant documents available and the number of unique topics that the documents cover. Below are some examples of what tasks each tool can help users accomplish.
ChatGPT can be seen as a virtual secretary, capable of managing users’ calendar, scheduling meetings, sending reminders, drafting emails, and more. It can also coordinate applications using its access to wearable devices and outside applications, like smartphones and calendars. Backward access in the other direction allows ChatGPT to surf through web pages and relevant applications created by users in their programming language of choice.
DeepSeek, on the other hand, can keep notes of discussions users wish to keep a record of, extract key points automatically, and flash back summaries later on request, or periodically, as reminders. It can also connect with the wider network of confidential documents and respond to user queries relevant to discussions or meetings. Unlike ChatGPT, DeepSeek does not need to be connected to other applications to accomplish all of these functions. The conversational functions of DeepSeek are also much more privacy oriented, requiring users to allow conversational access periodically, or after every certain number of questions, instead of allowing DeepSeek to be always actively listening like ChatGPT. This makes DeepSeek especially useful for sensitive conversations with commercial social media spying on the users’ actions.
Target Audiences
The intended audiences for DeepSeek and ChatGPT differ fundamentally. Consequently, both content creators and consumers will identify meaningful similarities and differences of usage between these advanced conversational AI tools. While they will address a diverse range of needs and wants, many specific user problems will not be solved by either tool. With these limitations in mind, interested audiences are presented with unique but allied experiences from both tools. All intent-focused users are looking for simple, informative answers to their queries. Clarification questions and follow-up instructions are terms of cat-and-mouse communication but the actual conversation does not tap deeper, collaborative intelligence-digging aspects of either intelligent assistant. What differentiates dialogue from intentionless search is the conversational aspect. Both must deliver on their specific promises to build rapport and user empowerment.
DeepSeek Users For DeepSeek, one cannot search non-intuition producing queries unrelated to human intent, become disappointed by the search result, and jump to the next SE. DeepSeek’s text-seeking efficiency and enhanced chat nicety reduce abort rates of retracing steps in asking and answering the iterative content-digging questions for those who would love to generate creative outputs. While often delivering a satisfactory answer at the click of a button, ordinary SEs cannot empathically encourage annoyed users with plenty of follow-up questions to explore their preferred content style and shape for an ideal result. The DeepSeek interface nurtures empowerment and document collaboration like a perfect agent of human knowledge workers. These cognitive-conversational ideas have slowly made their way into normal SEs, which provide paraphrased variations on found documents without generating sensical, suitable, coherent excerpts of such texts. Why not make the computer just fetch the text you love to cite, with, or against?
DeepSeek Users
DeepSeek works best for a particular set of users: information professionals and their end users in organizations. The former group, including Information Specialists, Research Scientists, Business Analysts, and Librarians, have expertise in finding answers to complex information problems, normally a few times per week or month. The end-user group is the general public, Information Edge Users, comprising staff in firms who have occasional, irregular, and discrete information needs, often expressed as questions, which cannot be answered by an in-house knowledge repository or search engines.
DeepSeek extends the capabilities of a search engine in two important ways. First, it reviews hundreds of thousands of document titles and retains the ones that are significant in their context. And second, it indexes the documents comprehensively, capturing the information within each in a structured way. Making semantic and context-sensitive use of Language Modeling, DeepSeek works like a consultant librarian. Particularly, it derives and organizes possible or conceptually similar questions that the user may wish to ask, which can be presented to explore or browse. For example, expecting follow-up requests about ‘US Real Estate’, it may infer and present possible users with answers to what type of buildings normally generate the highest rent, or what taxes are applicable to a landlord.
DeepSeek works in a more explicit, anticipatory, and planned manner. Thus, DeepSeek has the capability to support an Information Edge practical utility. Unlike search engines, which were long criticized for overly focusing on the precision-recall dilemma, at DeepSeek, precise answers are delivered for the major part of the user-requested information.
ChatGPT Users
Because ChatGPT was made available free of charge to anyone with an internet connection, it has rapidly become one of the most broadly used productivity tools on the planet. Whether the ridiculous content it generates and the fact that it has made its way into the lives of millions of users in such an incredibly short time is good or bad, remains to be seen. People use ChatGPT to provide code snippets; give business advice; generate lists; write marketing copy; draw-up privacy policies; write email copy; develop jokes; compose poems; draft screenplays; practice languages, and answer trivial questions. The nature of ChatGPT being a generative Large Language Model means that it can emulate human reasoning and conversation on demand, respond to a variety of prompts, and transform initial instructions into a desired output format. The ability of ChatGPT to continue to be useful in multiple domains and the relative ease with which it can be prompted makes it appealing to a large base of users. However, despite its wide applicability across verticals, people often complain about ChatGPT giving irrelevant information or failing to stay coherent for longer conversations. Hence, it is better to exploit the generative capabilities of ChatGPT for shorter tasks that can be reasonably encapsulated with standalone prompts.
Integration Capabilities
DeepSeek offers two basic ways to be integrated with the systems: Execution API and Web Interface Integration. Execution API is a common REST API. Developers can choose whatever programming language they think has best fit for the task to program server to server calls to DeepSeek. All DeepSeek plugins listed above are also provided in the API interface so it is possible to create the functionality exposed by these plugins from scratch and in any other manner that fits the business task to achieve. Web Interface Integration continues with the idea of no code integration and is a possibility to enable queries from any web pages. In this scenario, you choose the term you want to search for, and the interconnection of programs at the back end is put in place that is URL-based request to DeepSeek.
In addition to above two basic integration capabilities, there are plugins that are targeted to well known tools to help business users work faster. DeepSeek is integrated with best known tools. Once enabled on your messaging apps, DeepSeek adds buttons in messaging channels, enabling instant answers to questions for directly in a chat channel or conversation. Notifications posted to channels are interactive, allowing users to explore chatbots straight from those notifications. The integration requires no coding changes users can seamlessly explore DeepSeek modules without changing their user experience. Moreover, there is a planning phase to DeepSeek Connectors project around the pipelines between DeepSeek and external applications munching data in regular interval and pushing updates to DataLake and vice versa.
APIs and Extensions
Exploring the integration capabilities of DeepSeek and ChatGPT reveals key differences in approach and execution. DeepSeek boasts a more modern architecture than ChatGPT’s original APIs, allowing for internal APIs that serve internal extensions. Meanwhile, ChatGPT supports external APIs and a rich ecosystem of third-party plugins to enhance productivity and creativity.
DeepSeek serves APIs that allow developers to integrate any of its functionality in their own applications to create end-to-end custom solutions or combined services. Additionally, DeepSeek allows users to customize their models trained on their own data and serve them as APIs enabling customized model integration. Using the endpoints, they can configure as many instances as needed to serve different purposes. DeepSeek allows its services to be served through non-http protocols as well, opening it up to the IoT space. ChatGPT’s APIs integrate the model with external applications that complement ChatGPT or enable it to become multi-modal. However, customization is not supported. In the context of plugins, ChatGPT businesses integrate third-party services directly in ChatGPT, allowing it to perform complex workflows in collaboration with other services. It’s made possible through the tools and functions encapsulating the API calls and responses.
Third-party Tools
When it comes to using these tools, ChatGPT stands out with its myriad third-party tools that come built in with the product. While some are used commonly by everyone, others are used very rarely or are much more niche use cases. These tools have been useful for enhancing the core functionality of ChatGPT and allowing ChatGPT to help users with additional and specific tasks. So, while DeepSeek is focused more on answering complex queries in a better format with better and improved contextualized answers, ChatGPT aims to assist you with specific tasks that are enhanced through the use of these additional tools. If you need help with one of the task types made easier by ChatGPT’s third-party tools, then you’re better off visiting ChatGPT for now and utilizing its third-party tools. However, if you need help tackling a much more complex use case that requires you to loop in multiple sources of information and analyze them to answer a highly contextualized question, then DeepSeek is the move.
Cost Comparison
Cost Comparison In most case scenarios, large organizations and corporations are willing to invest significant resources into advanced machine learning systems. In those cases, both approaches to creating such models are comparable. This is not the case with most of the rest of the market. Companies that are using ChatGPT for practical tasks are usually limited to its free API and the limitations of its free API do not exempt them from monthly billing after certain paid usage capital incurred. The Free API, for example, allows 1 million tokens of usage for free and then a fee for each additional token. The Paid API allows for a certain number of tokens sharing a lump sum just for January 2024. The limits of ChatGPT Free, however, are severe: 2 prompts every 3 hours and one additional prompt for every up to 16 tokens per prompt or response. DeepSeek, on the contrary, has 100% free use as long as all inputs are files of type PDF, TXT, JSON, CSV or HTML and queries are simple text queries. The DeepSeek product is free beta because it is virtualized and needs to be proven at larger scale while ChatGPT is virtualized and running cloud system and can earn revenue. In other words, while DeepSeek is a product that is built to serve people, ChatGPT is rapidly becoming an advanced system built by people to serve whoever can pay at least a little or an even larger quantity of tokens. On the other hand, people feel comfortable with paying something for a service they use than for a permanent free option, which creates doubts about the development and the quality of the resource.
Pricing Models ChatGPT makes the lion share of its revenue profits from SaaS-based deployment through its advanced API. DeepSeek charges only for JSON and CSV ingestion because these take considerable time to process, but overall, charges nothing to the end-users with text search queries, or users whose uploaded content is HTML or text files. For both search and upload, DeepSeek offers lean Cloud deployment that needs to be proven at larger scale. The DeepSeek paid model is unconventional because the aim of the deployment is to create a free base of beta users inside corporation firewalls, which are notoriously impossible to enter. The aim of the deployment…
Pricing Models
While both ChatGPT and DeepSeek are sophisticated AI systems based on powerful deep learning models trained on massive data, there are significant differences between both available models. The most critical difference between ChatGPT and DeepSeek from a user-focused point of view are their pricing models. ChatGPT is a generative pre-trained transformer model in the public domain, where it can be accessed via free accounts. In addition to the free plan, ChatGPT also provides subscription plans, plus for a monthly fee, and enterprise plan for custom pricing for teams, which not only have additional features but also utilize state-of-the-art deep learning models requiring high computational power.
DeepSeek, on the other hand, is a paid service without free access, where users cannot simply sign up to make arbitrary requests. It is instead charged as a pay-as-you-go subscription service for standard chat models. Tokens are used to measure the amount of input and output of the models, with one token corresponding to approximately 4 bytes of text in English or 0.75 words, where a token includes troublesome punctuation, or characters that are limitations of delta files, which are a mechanism for maximizing the compactness of text files used by deep learning models.
With a pricing model based on an API service architecture, DeepSeek is mainly catering B2B market focusing on product integration and platforming to allow for customized systems design. ChatGPT is catering B2C users with a pricing model that allows for real time interaction with a prebuilt state-of-the-art deep learning model that is easily accessible from anywhere. These business models are of course tied to the nature of task that each of these deep learning models were trained on; generative text generation, in the case of ChatGPT, and information retrieval, in the case of DeepSeek.
Value for Money
As mentioned, pricing is often based on a variety of parameters. For instance, the text generator charges users based on a token pricing. For the vanilla model, up to 4097 tokens (input plus output) cost USD 0.002 per 1000 tokens. It means that if you put a prompt of 4000 tokens (which is required for a lengthy discussion) and request an answer of 10 or 20 tokens or more, the cost is 0.002 or around 0.003, which is dirt cheap. As a single instance, it means that if you write and get a response in the same terminological range of 2000 tokens, it can cost you USD 0.01! Depending on a different API calls model, the pricing based on the top–up agreement model includes calling conversation and result fulfillment costs. The former nears USD 0.01 with an average of two requests during a chat. The 100 response token fulfillment is also around USD 0.001. As a result, the average fee approaches USD 0.02 for a comparable result.
But do cheaper prices really mean value for money? After all, both systems differ substantially functionally and in terms of generated output quality. In a nutshell: In some cases, when no special configuration is needed, one can be said to be rather more accessible and cheap. In some other cases, if a quality output is needed and/or you lack a special instruction, the other is far better–value. Its pricing reflects the additional proprietary high value technology that allows quality weighting to work. It drastically improves results for users–companies, especially when data size is larger and queries of rather short completion.
User Feedback and Reviews
User reviews are important for indexing users’ satisfaction with the models. Therefore, reviews for both DeepSeek and ChatGPT are presented below. To summarize: both models have loyal users, but while DeepSeek users review it positively for complex, highly specialized queries, ChatGPT has a much broader application range, being preferred for day-to-day conversations and general assistance, especially for creative writing.
DeepSeek Feedback Feedback on DeepSeek is limited given its recent launch and specialized purpose. However, DeepSeek makes ambitious claims regarding the complexity of the tasks it was designed for in comparison to ChatGPT. For example, an individual states: “I would love to know how DeepSeek is different from ChatGPT. I tried asking DeepSeek complex programming questions and good explained answers it managed (I was impressed). For my task it possible utilizes Bing, ChatGPT is asking simple questions back.” and that “for coding questions DeepSeek gives much better answers than ChatGPT” and “Simply DeepSeek is powerful for my specific task which is answering complex programming requests/descriptions. I haven’t tried any other complex tasks.” An expert claims DeepSeek and other chatbots “have huge issues with consilience. This is a great thing to point out, because most chatbots just give up at the idea of explaining things”. Another states “DeepSeek can answer more cryptography questions than ChatGPT — partly because ChatGPT refuses to answer some questions.” Finally, an expert states: “It’s heartening that there are now advanced retrieval systems like DeepSeek that can plug into chatbots. Retrieved contextual information goes a long way to alleviating the limitations of small ctx chatbots.”
ChatGPT Feedback Feedback on ChatGPT is extensive given the number of years it existed, and over 10 million users reported reviewing their experience. A recent study ranked ChatGPT 6th on the list of the most used websites, i.e., higher than Facebook and Twitter. While less than a decade old, ChatGPT usage is unsurprisingly peaking, being the 9th most visited website, and the first with no ads.
DeepSeek Feedback
DeepSeek is currently in beta, released in October 2023. But users speak favorably about the offering on social media. DeepSeek wants to be used by students, teachers, researchers, and professionals looking to uncover insights from text for study and work. It gets to the essence of lessons before or after study, at exam prep, project and paper prep, for discussions and group work, and other assistive and social uses.
DeepSeek users are already creating “exam prep shortcuts”, working out study collaboration tips, and outlining topics in their use of the generative text search engine. They love DeepSeek because it extracts concise answers and insights, summarizes concepts, provides explanations, and clarifies topics. Explanations and topic elaborations generally return no more than a paragraph.
DeepSeek is timely. Generative tools are changing how we process text, and educators want to know how these tools will be used in the classroom. Teaching assistants as essay graders today in some cases will become cognitive assistants for instructors and students tomorrow. Engaging and guiding questions to DeepSeek that users have discovered themselves will prepare a meta-introduction to study and, in some cases, replace slides at lectures, and notetaking during lessons. Research assistants for preliminary studies will make changes in practice more student-centered than before.
ChatGPT Feedback
“ChatGPT is extremely useful for managing emails/quick messages, generating code, language creation, and collaboration in creativity; when it comes to simple, ordinary replies, like how to perform a given calculation or translate a sentence, it’s not useful at all; it also has no information update since 2021, so it performs poorly on current events!” – A short feedback on ChatGPT; an example of both positive and negative aspects.
Although most of the reviews are just great and users praise it for being very useful and a great help for their creative processes, there are some negative feedbacks as well. Following are some collected feedbacks and reviews on ChatGPT to have a consensus of the user’s satisfaction regarding ChatGPT.
Additionally, ChatGPT’s responses can suffer from harmful logical fallacy issues if they request highly instructed task completion and also rely on fallible external systems. Framing the prompt correctly is crucial for receiving a helpful response. ChatGPT can also produce short creative items based on given input, e.g., write a poem for a given location or write a pattern for a romance story. Nevertheless, most of these items are very low quality. Another limitation of ChatGPT is its large memory; it cannot see an entire novel or long paper submission and will not generate convincing story/plot elements, e.g., long articles, and novels.
Some users express sorrow regarding how ChatGPT has corrupted original plots and ideas of their favorite stories.
DeepSeek Roadmap
It’s not my place to define future developments and roadmap of any AI-based application or product. What I can do is to inform what is to be decided by the team of members and designers behind DeepSeek. From my knowledge of the system from a technical perspective, as well as training, I would say that some essential guidelines could be the following:
– Individual Multimodal applications. Extract&Share for graphics; WebDeepSeek for browsing; and others on Streamlit for DeepSeek.
These are for us the main applications based on the DeepSeek architecture. Given the impressive results they already produce, it would make sense to further research them, to enrich their functionality, ease of use, and UI usability as soon as possible. For Extract&Share, further possible developments are mentioned below.
– Explore Image-Visual Features. Better clarify if your image is suitable for visual search capabilities or it is simply a visual feature used to represent an answer.
These capabilities, that were basically developed for Extract&Share deepseek instance only, might even be the basis of further funded initiatives in the press, advertising, and graphics domain applied research.
– Merge All DeepSeek Instances. The simplest but also the more demanding and promising next step could be to install on a single instance, either locally hosted or in a cloud-based solution, all DeepSeek instances. The system would be able to provide users with the capabilities of each one of the listed DeepSeek applications, with its own dedicated UI and functionalities.
For this perspective, the special DeepSeek API, currently in final beta phase, is enabling the tight access to and combination of the DeepSeek pipelines currently in place for the mentioned applications.
ChatGPT Roadmap
OpenAI has done relatively good job sharing its future plans for GPT-4 and beyond, though many plans appear vague at this point. Here are some key areas of planned development:
Multimodal Models, Training and Using: Future GPT versions will support more modalities with specific focus on video. Furthermore, training new models will become much faster and less expensive thanks to other channels, faster, and less expensive methods.
Long-Context Training: An important area of potential future development is – adding support for training and using “long-context” models that – multi-thousand length of tokens.
Improvements in Base Capabilities: OpenAI plans continuous improvements in base functionality of the model. In addition to better comprehension and less hallucinations, the areas for improvement include factuality control, especially for mathematical and programming tasks; better reasoning especially for tasks requiring steps or spatial awareness; reliability, better support for hard memory tasks such as legal documents; supporting delicate real-world concerns better; continuing better support for languages and cultures outside of the English-speaking world.
Improved Content Filtering and Moderation: A sensitive area of prompt engineering and conversation design is prompt filtering to ensure even toxic prompts do not lead to toxic responses when it’s obvious. Related to this is moderation. Also, it’s especially important for GTP-based solutions, since they may get prompt ready to reverse prompt attack for academia, secret government documents, sensitive business agreements for M&A, and Greenfield Approach to M&A.
Dialogue, Memory and Continuous Learning: Future iterations of ChatGPT will continuously learn to improve discussions over time, and even store personal memory to better suit individual user needs and preferences.
Ethical Considerations
Creating technology that is both ethical and responsible can have negative effects, but it must be prioritized. Natural language generation systems can be used at scale—which provides significant advantages, but also magnifies the risks associated with any flaws in the technology. Additionally, the datasets that underpin these kinds of technology can encapsulate many problematic elements like bias, misrepresentation, and data privacy issues. These ethical challenges mean that products built on these models should be treated with care.
Data Privacy Models are not inherently designed for privacy. While onboarding developers and businesses, a policy that entropy-based prompts that can identify an individual’s data are not allowed is enforced. To further control for abuse, developers were told to curate certain kinds of questions that users can take these systems through. The LLM has several issues that can encourage users (especially given these systems are often fine-tuned for chat) to divulge sensitive-private information. This can inadvertently create a feedback loop where users feed these models the exact information that would identify them. To address these and other privacy concerns, a fine-tuning or training technique called “instructed finetuning” (IFT) is now offered. Through IFT, businesses can get their data securely inside the model while maintaining both data privacy and relevance.
Bias and Fairness LLMs are known to be biased due to the data they’re trained on, which often include countless negative examples. For example, they have been known to create prompts that are racist, sexist, and overall biased. These biased performances can occur even in rare occurrences through zero or one-shot feeding, or they can occur in the few-shot feeding context as well. Moreover, LLMs struggle with computing differences in accuracy across certain demographics. Given the present-day biases in the news and on social media, it’s difficult to stress-test and evaluate which LLM performs best across which demographics. In short, there’s no real way to know ahead of time which language model is more likely to output a biased text for a specific demographic.
Data Privacy
Data Privacy is one of the biggest differences between DeepSeek and ChatGPT. The kind of documents you can ask DeepSeek questions about involve sensitive, private, proprietary, and confidential information. The data sources that are searchable with DeepSeek are typically uploaded by clients to their own DeepSeek instance. They have control, ownership, and sole access to these documents, making DeepSeek 100% compliant. DeepSeek only stores indexing information of the documents that are searchable for using the solution, and does not index the documents themselves so that DeepSeek has no access to the data in those documents.
In contrast, ChatGPT requires sending the prompt as well as the document content when asking questions. This means that the prompt and the document content would be used to generate responses. ChatGPT historically has not been compliant even though they recently announced that their enterprise version is compliant. That says, in the case of the enterprise version, clients that input data for the model will have control, ownership, and sole access to such data, assuming they sign the right contract and pay the enterprise fee per month. Regardless, this has been one of the biggest limitations for ChatGPT’s consumer-facing product up until now, as it is widely still being used for the purpose of dialogue and creative writing. Up until recently, ChatGPT has not only been sending users the same wealth of knowledge they have been providing for free, in addition, it has been identifying particular users through assigned user IDs and storing tokenized versions of the chat content against their user profiles in a dataset that is being used to further fine-tune their models.
Bias and Fairness
The topic of bias in data-driven systems has received growing attention. Bias can have various causes and types of definitions. Here, we consider bias as the failure of models to represent different groups of humans fairly. For example, consider a model that was trained on disproportionate data from people using English. The model could perform poorly or in unexpected, biased, or unfair ways for people from some underrepresented ethnicities or countries. Bias according to this definition can also be seen as an issue of both inefficiency and inaccuracy, as models that are biased could be offending and unhelpful to some groups.
What kind of biases could large AI models encounter? Widespread examples from the past are racist or sexist sentiment analysis results based on biased training datasets. These datasets reflected the actual online data and conversations curated by the companies that built the algorithms, but they also displayed societal biases that surrounded them. Or use harmful cliched descriptions that suggest people in some groups conform to certain stereotypes. But in addition to capturing existing social injustices surrounding us and in our data, one could also consider biases from target groups underrepresented in the model’s training data – considering people that are not or are only poorly represented in the training data to be potential target groups for bias detection. Moreover, models could be incapable of recommending items from certain groups of humans or making profit decisions from certain user groups, whether these biases correspond to their datasets or not.
Case Studies
This section shows two specific examples, aimed at directly comparing the performance of DeepSeek and ChatGPT in a real-world application: an R&D report about the use of a genetically modified cyanobacterium for the self-synthesis of biologically active compounds. This research task focused on finding alternative, clean and sustainable sources of biologically active compounds for the pharmaceutical and nutraceutical industries. DeepSeek Case Study For the DeepSeek search, the exact name of the genetically modified cyanobacterium was included as keyword. The search was conducted on three types of databases. The query was executed on April 21, 2023. The results were filtered by type of publication, document type, language, publication year and available full text. ChatGPT Case Study ChatGPT was asked to generate an R&D report on the same topic, but more details were provided about the whole task. The text was compiled with the interactive mode of ChatGPT in two parts. The first part was generated in a chat conversation, in which the main topic was discussed and explored. Then, the prompt about the R&D report was provided to generate the second part of the text. The generated text was checked using available tools to assess the possible mismatching with available sources, and the necessary corrections and revisions were applied to obtain the final version. The selective automatic editing made it possible to address possible semantic unilaterally or biases of the AI tool used. The results of the tests with the two above-mentioned approaches are discussed below.
DeepSeek Case Study
When we originally envisioned DeepSeek, we aimed to create an internal tool for our development teams to experiment with and measure the value of the own data. As a deeply technical team with decades of experience in machine learning and systems architecture, we simply thought our solution would be useful, and hoping to discover and build further integrations or endpoints with customers. On this initial phase, we were surprised how scientific DeepSeek felt, and then how much economic value generated exploring our entire knowledge and development data base. The design of the tool was so organic for us that it was used to shape and cement our roadmap and technical vision in the first months of development.
Our technology draws inspiration from biological evolution, namely from the concept that surprise generates exploration. All existing systems search for most likely answers, and thus limit exploration of large data spaces. DeepSeek expands the search space, and aims to provoke surprise responses that can be valuable for the business. Our approach entails a data-centric AI pipeline for retrieval-augmented generation models that combines the use of coalescing knowledge graphs with the training of novel pipelines based on pseudo-instruction-finetuning using user selection on model responses. We validate the user selection via answerability metrics, and learn to select the most interesting information for training our internal domain-customized generative models.
To the best of our knowledge, DeepSeek pioneered building a tool that creates proprietary training data by selecting preferred responses from a foundation model powered by our proprietary retrieval backend, specifically built for our multi-modal, multi-process, enterprise-level rich-context data storage, pipeline and workflow.
ChatGPT Case Study
To produce high quality and consistent text from ChatGPT, we started with shorter prompts. And we interactively refined those prompts while in conversation with ChatGPT to get better quality output. Next, we provided a bigger context to ChatGPT in these refined prompts to produce longer and complete sections of text on the essay outline topics. Overall, our goal was to make the conversation natural and hence prompt less for long-form authoritative text generation. We also used ChatGPT’s built-in message summarization capability several times to shorten the conversation history in order to provide more context to it without hitting the context length limit.
We noted early on that ChatGPT was frequently giving boilerplate responses to our asking for overviews about NLP and LLMs. The response is what we consider a boilerplate response, because it lacks key aspects of ChatGPT that would be important to a researcher who is our audience here.
The benefit of using ChatGPT over existing search engines is the natural conversation flow you can have with ChatGPT, which makes it easier to iterate in querying it. Our experience has been that performance of LLMs such as ChatGPT have reached a degree where they can be interacted with in a conversational manner to produce high quality academic and technical content across many different topics.
Expert Opinions
In general, the landscape of information retrieval systems for the professional sector is evolving towards Natural Language Processing (NLP) enhanced systems. ChatGPT is an extraordinary NLP driven search engine that has become available for the general public since the end of 2022. Its popularity has risen sky high as it delivers to users answers in a conversational language.
DeepSeek, a product of QikPod Technologies and launched in Asia in early 2023, is a paid service catering to the professional sector. It is an AI tool using Large Language Model (LLM) for search among enterprise databases to help users and organizations to become more productive, save costs and time, and help scale business using its LLM driven search among enterprise databases. We recognize the merits of ChatGPT, but caution users that ChatGPT and similar AI tools are likely to be flawed in their results. An especially harmful outcome could be giving financial advice based on false facts.
DeepSeek addresses both these concerns. The NLP model used by DeepSeek is trained on data specific to the enterprise and tuned to give results from the enterprise database chosen as the backend. Thus, it is less likely to spout inaccuracies, as the data it has been trained on is specific to the enterprise, and the backend database chosen contains data verified by the enterprise as correct. In this dialogue, we address the differences in both tools, aimed towards the users who are confused regarding which search engine to use, and under what circumstances should they choose either DeepSeek or ChatGPT.
Industry Insights
As an emerging technology, DeepSeek’s market availability is more limited than ChatGPT. However, it is rapidly gaining traction with enterprise customers seeking efficient data insights that only DeepSeek’s technology can provide. These differing characteristics motivate particular use cases. Research & Development, Product, Marketing, Knowledge Management, Customer Service, and IT Helpdesk teams use DeepSeek to find answers hidden in the enterprise document repository, thereby improving the speed and accuracy of their decision-making. These work functions are typical of knowledge workers who must extensively read documents to discover and assemble information. An example is a healthcare company seeking to distill the details of a new protocol from multiple product labels. Using DeepSeek, a typist would input a key question within the DeepSeek user interface. The typist would then scan the answers returned in context for the one that best satisfies the request, and click to display the answer in full context. DeepSeek would condense the relevant protocol information, making it much quicker for the knowledge worker to search through the enterprise’s document repository and store it for later use.
Academic Perspectives
Academic work has begun to take these and other emerging AI tools seriously, and input from scholars in relevant areas of study provides an important and difficult check on my own irresponsible enthusiasm. The first peer-reviewed effort to frame the edifice of AI-assisted writing represents an extended discussion. It charts a history of writing assistance and compares the specific variant of ChatGPT currently deployed to assist users with previous eras of physical and software-based writing assistance. In doing so, it distills a series of criteria by which to evaluate these tools: firstly, does the tool intervene diachronically in and alter the writing process? Secondly, does the tool provide user-initiated feedback or “suggestions?” Thirdly, does the tool change the structure of the text? Fourthly, does it alter the expression of the text (style, coherence)? Lastly, does the tool function as an agent that authors the writing?
Diachronic versus synchronic action is at the heart of the analysis. According to these criteria, writing tools can be grouped into two classes, depending on whether they intervene in the writing process as a whole, or influence more synchronic qualities of the text being written, such as its grammar, coherence, or style. Classical writing assistants operate diachronically, creating an auto-production loop in which users interact with a software tool to revise and improve the writing product over time. Previous generations of organic and digital tools such as spell-checkers, grammar checkers, and software act synchronically, and do not guide the overall process of writing. Their functional similarity with actual human feedback has led to an overloading of terms such as “feedback” and “suggestion.” Guidance reflects both a diachronic and neuro-cognitive input into the intervention, which is absent from synchronous actions.
Conclusion
In conclusion, there are key differences between DeepSeek, a specialized document search tool for deep web search, and ChatGPT, a natural language generative tool. Each tool provides novel experiences to their users. The main benefit of DeepSeek is its ability to return results from databases that store coded and timestamped documents. The main advantage of DeepSeek is that it uses natural language processing and machine learning to display only the best results for any given query while still taking care to preserve covertness. ChatGPT is a natural language generative tool that interacts in conversational dialogue form, enabling a natural back-and-forth exchange similar to the give-and-take of human communication. This tool can also be fine-tuned to match the style of a particular writer. ChatGPT’s natural use of language has some significant limitations. One concern is that values reflected in the text are based entirely on the information fed into the system. Thus, among the biases may be societal biases mirrored in the training data, and bias introduced by sampling and evaluation protocols that overemphasize certain aspects of writing.
If you’re looking for a tool to find timestamped and coded documents from databases of the deep web, DeepSeek is the choice; however, if you wish to find information on the internet and engage in chat discussions about specific topics based on the results, ChatGPT is the more fitting choice. DeepSeek currently doesn’t hold or track any user data. While ChatGPT privacy policy mentions that all messages sent to ChatGPT may be reviewed by the staff for quality and safety in order to improve ChatGPT and services.
Founder of EonixMedia, Sameer Alam brings a wealth of experience in media and digital innovation. With a background in strategic leadership and creative vision, he drives forward-thinking solutions in the ever-evolving media landscape.