原版：https://blog.csdn.net/abcdeWA/article/details/145649294
THE COMPLETE GUIDE TO DEEPSEEKAI PRECISION USE (FULL VERSION)
I. DEEPSEEKAI BASIC COGNITION (1500 WORDS)
1.1 TECHNICAL ARCHITECTURE ANALYSIS
* Transformer Model Principle: The Transformer model abandons the sequence processing methods of traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs) and adopts a self-attention mechanism. This mechanism allows the model to efficiently capture long-distance
dependencies by calculating the degree of correlation between each location and other locations in parallel when processing sequence data. For example, in a natural language processing task, the semantic connections between words that are far apart in a sentence can be accurately grasped. Taking
a translation task as an example, a noun in a sentence in the source language may have a key logical relationship with a verb that is far behind, and the Transformer model can quickly identify and use this relationship to accurately translate.
* Multimodal capability implementation path:D eepSeekAI achieves multimodal capability by fusing data features of different modalities. For the image mode, the convolutional neural network is used to extract the visual features of the image. For the text modality, the semantic features are obtained
through the word vector embedding and the Transformer layer. Then, through the cross-modal interaction module, the features of different modalities are fused and aligned. For example, in the image description generation task, the visual features of the image are extracted first, and then combined
with the pre-trained text model, the text that accurately describes the content of the image is generated. Practical case: In the medical imaging diagnosis assistance system, medical imaging (such as X-ray, CT, etc.) and related medical record text information are combined to provide doctors with
more comprehensive and accurate diagnostic suggestions.
* Knowledge Distillation and Continuous Learning Mechanism: Knowledge distillation is the process of migrating knowledge from a large teacher model to a small student model. By having the student model learn the output probability distribution of the teacher model, rather than just learning the
real labels, the student model can achieve performance close to that of the teacher model at a smaller scale. Continuous learning allows the model to learn from new data without forgetting old knowledge. For example, in the news information classification task, as new news events and topics
continue to emerge, the model adapts to new categories and characteristics without losing the ability to classify previous news through a continuous learning mechanism.
1.2 FUNCTIONAL BOUNDARY DEMARCATION
* Text Generation Capability Matrix (Creative/Technical/Academic): When it comes to creative text generation, DeepSeekAI is capable of generating imaginative stories, poems, ad copy, and more. For example, generate promotional copy for a travel company that highlights the unique charm and appeal of
the destination. In the field of technical text generation, it can write technical documents, code comments, and more. For example, generate detailed functional documentation based on a piece of code logic. In terms of academic text generation, he can assist in writing literature reviews, partial
chapters of research reports, etc. Case Study: A researcher used DeepSeekAI to quickly generate a first draft of a literature review on AI algorithm research, saving a lot of time in reviewing and organizing data.
* Data Analysis Processing:D eepSeekAI can analyze and process both structured and unstructured data. For structured data, such as sales data in Excel sheets, it cleans the data, analyzes statistically, and generates visual reports. For unstructured data, such as customer reviews, it can do
sentiment analysis, topic extraction, and more. For example, analyze user reviews of a product on an e-commerce platform to understand user satisfaction and key concerns.
* List of supported languages for code generation: Supports a variety of popular programming languages, including Python, Java, C++, JavaScript, and more. In practice, developers can input functional requirements described in natural language, and DeepSeekAI can generate code snippets for that
language. For example, enter "Create a simple Python function to calculate the sum of two numbers" and the model generates the correct Python code implementation.
1.3 PERFORMANCE PARAMETER INTERPRETATION
* Context Window Management Strategy: The context window determines the range of text lengths that the model is capable of processing. DeepSeekAI effectively expands the context window by optimizing memory management and attention calculations. For example, when dealing with the task of sequeling a
novel, a large context window allows the model to better understand the previous plot and generate more coherent and logical follow-up content. Practical example: In legal document processing, the long context window enables the model to accurately understand the background information and clause
details of the entire case, so as to provide more accurate legal analysis.
* Response Delay Optimization Principle: Reduce the response latency of the model through hardware acceleration (such as GPU clustering), algorithm optimization (such as reducing unnecessary computing steps), and distributed computing. In real-time interactive scenarios, such as online customer
service chats, fast response speed can improve user experience. For example, after a user asks a question, the model can give an accurate answer in a short period of time.
* Multi-round dialogue attenuation curve: In multiple rounds of dialogue, the performance of the model may decay to some extent as the number of dialogue rounds increases. This is due to factors such as information accumulation and noise interference. By introducing a memory mechanism and a
conversational history management strategy, DeepSeekAI tries to slow down this attenuation as much as possible. For example, in multiple rounds of conversations between the agent and the user, the model can always maintain a clear memory of previous questions and answers, providing consistent and
accurate service.
Industry Benchmark Data: On text generation tasks, DeepSeekAI scores high on the novelty metric of creative text generation at [X]%, and on the accuracy of technical text generation, it can achieve [X]%. In terms of data analysis processing speed, the average time taken to process large-scale
structured data (such as a database of millions of records) is [X]% faster than the industry average. In the code generation task, the syntax correctness of the generated code reaches [X]%.
II. PRECISE INPUT METHODOLOGY (2500 WORDS)
2.1 STRUCTURED PROMPT ENGINEERING
* CRISP Framework Practices (Context/Role/Intent/Specification/Parameters): In practice, it is important to be clear about the context of the input. For example, in a medical consultation scenario, provide contextual information such as the patient's basic medical history and symptoms. When you set
a role, you can assign a model to play the role of a professional doctor. In terms of intent, clearly state whether you want to receive a diagnosis or a treatment plan. The specification requires that the format of the output be explicit, such as in the form of a text paragraph or a list of
bullet points. Parameter settings can adjust the length of the generated text, tone, etc. Case Study: A patient consults DeepSeekAI about headaches, and enters them according to the CRISP framework: the context is a frequent headache with mild nausea in the last week; The role is that of a
neurologist; The intent is to obtain an initial diagnosis and recommendations; The specification requires that the output be in the form of concise bullet points; The parameter settings are of moderate text length. The model gives targeted diagnosis and recommendations.
* Chain-of-Thought Design Template: The Chain-of-Thought design is designed to guide the model to think about the problem step by step. For example, in solving a math problem, the question is asked, and then the model is guided to list the steps to solve the problem, and finally the answer is
derived. The template can be: "Problem description -> First step idea -> Second step idea -> ... -> Final answer". Practical case: For a complex geometric proof problem, the model is deduced step by step through the input of the thought chain template, and the complete proof process is
successfully given.
* Negative cue elimination: Use negative cues when you don't want the model to generate specific content. For example, if you don't want to have false information or sensitive words when generating a news story, you can explicitly tell the model to exclude them. Practical case: When generating
corporate promotional copy, negative prompts are used to exclude inappropriate expressions related to competitors to ensure the professionalism and positivity of the copy.
2.2 DOMAIN ADAPTATION TECHNIQUES
* Terminology in Medical Biology: In the field of medical biology, it is important to use accurate terminology. For example, in the diagnosis of diseases, it is necessary to use internationally accepted medical terms, rather than using colloquial terms at will. The model is trained on a specialized
medical biology corpus to understand and apply these terms accurately. Practical case: In the writing of medical research reports, the model accurately used professional terms such as "coronary atherosclerosis" and "apoptosis" to improve the professionalism of the report.
* Special format requirements for legal documents: Legal documents have strict format specifications, such as complaints, contracts, etc., all have a specific structure and order of terms. DeepSeekAI can generate compliant legal documents in accordance with these format requirements. For example,
if a lease contract is generated, the model will be generated in a standard format such as the beginning of the contract, the subject matter of the lease, the rent and payment method, and the rights and obligations of both parties.
* Rigor control of scientific research papers: Scientific research papers need a high degree of rigor, including data citation, reference format, etc. When generating relevant content for scientific research papers, the model can follow academic norms and accurately cite data and references.
Practical case: A researcher used a model to generate the experimental results of the paper, and the model accurately presented the experimental data and analysis conclusions in strict accordance with the format and specifications required by the academic journal.
2.3 MULTIMODAL INPUT OPTIMIZATION
* Image Annotation Best Practices: Adopt precise annotation tools and methods in image annotation. For example, use professional image annotation software to accurately classify and locate objects in images. Labeling follows a unified labeling standard to ensure the consistency of labeling.
Practical case: In the image annotation of the autonomous driving dataset, the vehicles, pedestrians, and traffic signs in the road scene are annotated according to the strict labeling specifications, so as to provide high-quality data for subsequent model training.
* Tabular Data Cleaning Criteria: For tabular data, check the integrity of the data first and fill in the missing values. Duplicate data is then processed to remove redundant records. Normalize the data, such as the unified date format and numeric units. Practical case: In the processing of
enterprise financial data, the chaotic financial statement data is sorted into a standardized and unified format through data cleaning, which is convenient for subsequent analysis and modeling.
* Snippet contextualization: Provide sufficient context information when entering a snippet, including the project environment in which the code is located, relevant libraries, and dependencies. This allows the model to better understand the intent of the code and generate more accurate and
relevant code. Hands-on example: When developing a web application, enter a snippet of front-end JavaScript code and explain its position in the overall page layout and interaction logic, and the model generates the back-end interface code to match it.
3. ADVANCED FUNCTION TUNING (3000 WORDS)
3.1 API DEEP INTEGRATION
* Asynchronous call performance optimization: In large-scale data processing or high-concurrency scenarios, asynchronous call can significantly improve system performance. By putting API requests in a queue, you can let the main thread continue with other tasks and avoid blocking by waiting for an
API response. For example, in the data analysis system of an e-commerce platform, a large amount of order data needs to be analyzed in real time, and by asynchronously calling DeepSeekAI's API, the system can complete the data analysis task without affecting the normal business process.
* Streaming response processing solution: For long text generation or large volume processing tasks, streaming response can allow users to obtain partial results faster and improve the user experience. In the process of generating the result, the server gradually sends the generated part to the
client. For example, when generating a long-form news story, the client can see the content generated by the model paragraph by paragraph in real time, rather than waiting for the entire story to be generated.
* Multi-model collaborative workflow: Combine multiple models according to different task requirements. For example, in a task that combines image recognition and text generation, an image recognition model is used to classify and extract features from an image, and then this information is passed
to a text generation model to generate a detailed description of the image. Practical case: In the intelligent advertising production system, the image recognition model is used to analyze the characteristics of product images, and then combined with the text generation model to generate
attractive advertising copy.
3.2 PARAMETER FINE-TUNING GUIDE
* Temperature Dynamic Adjustment Strategy: The Temperature parameter controls the randomness of the generated text. When generating creative text, the temperature value can be appropriately increased to increase the diversity and innovation of the text. And when generating text that requires
accuracy, such as technical documentation, lower the Temperature value. For example, when generating a poem, setting Temperature to [X] will result in a more creative and unique poem; When generating the specification, set the Temperature to [Y] to generate more rigorous and accurate text.
* Top-p Sampling Optimization Curve: Top-p sampling generates text by selecting the most likely words with a cumulative probability of reaching a certain threshold, such as 0.9. Depending on the task, this threshold can be adjusted. When dealing with open-domain conversations, the threshold should
be appropriately raised to make the generated replies more in line with natural language habits. When dealing with specialized domain tasks, lower thresholds to ensure the accuracy of the generated content. Practical example: In daily chatbots, the Top-p threshold is set to [X], and the replies
generated are more natural and fluent; In the medical Q&A system, set the threshold to [Y] to ensure the professionalism and accuracy of the answers.
* Penalty Factor Combination Formula: Penalty coefficients are used to avoid repetitive or nonsensical content generated by the model. By adjusting the combination of different types of punishment coefficients (such as repetitive word punishment, low-frequency word punishment, etc.), the generation
effect can be optimized. For example, when generating stories, the penalty coefficient of repeated words should be appropriately increased to reduce the repetition of vocabulary and plot in the story. When generating texts with a large number of technical terms, the penalty coefficient of
low-frequency words should be reduced to ensure the correct use of professional terms.
3.3 PRIVATIZATION DEPLOYMENT SCHEME
* Hardware Resource Configuration Matrix: Determines the configuration of hardware resources based on business scale and performance requirements. For small businesses or low-concurrency use cases, you can choose a server with a medium configuration, such as one with [specific CPU model], [specific
memory capacity], and [specific storage capacity]. For large enterprises or high-concurrency scenarios, you need to build cluster servers with high-performance GPU-accelerated computing. Case Study: When a small e-commerce company deployed DeepSeek AI privately, it chose a moderately configured
server based on its business traffic and data processing needs to meet the tasks of daily product description generation and customer inquiry response.
* Domain model fine-tuning process: First, a large amount of data in a specific domain is collected and the data is preprocessed, including cleaning and annotation. The pre-trained model is then used as a foundation to fine-tune the training on the domain data. For example, in the financial field,
a large amount of financial news, financial reports and other data are collected, and the model is fine-tuned to make it more suitable for the language style and business needs of the financial field.
* Security audit checklist: Formulate a security audit checklist, including data access control, data encryption transmission, and model vulnerability detection. Conduct regular security audits of the system to ensure data security and compliance. For example, check that data is encrypted in
transit and at rest, and that user access to model APIs is strictly authenticated and authorized.
IV. INDUSTRY APPLICATION CASE COLLECTION (3000 WORDS)
4.1 FINANCIAL RISK CONTROL SCENARIOS
* Financial Report Analysis Prompt Template: Design a special financial report analysis prompt template to guide the model to extract key information from the financial report, such as revenue, profit, assets and liabilities, and analyze and interpret it. For example, if you enter "Please analyze
the [company name] [year] financial report, extract the revenue growth trend, the main profit sources, and the change in the asset-liability ratio, and give a brief evaluation", the model can generate a detailed analysis report. Practical case: An investment company used this template to analyze
the financial reports of multiple listed companies, providing a strong basis for investment decisions.
* Risk early warning rule chain: Establish a risk early warning rule chain to discover potential risks in a timely manner based on different risk indicators and thresholds. For example, when an enterprise's debt ratio exceeds a certain threshold and its cash flow is abnormal, a risk warning is
triggered. Through real-time monitoring and analysis of large amounts of financial data, the model can quickly and accurately send out early warning signals. Practical case: A bank successfully identified the upward trend of credit risk of an enterprise by using the risk early warning rule chain
and took risk prevention and control measures in advance.
* Compliance Review Workflow: Develop a compliance review workflow to ensure that financial business operations comply with laws, regulations, and regulatory requirements. The model can perform compliance checks on loan contracts, transaction records, etc., and automatically generate review
reports. For example, check whether the loan contract contains the necessary legal terms, whether the transaction complies with anti-money laundering regulations, etc. Practice case: A securities company conducts compliance review through a model, which improves the efficiency and accuracy of the
review and reduces the compliance risk.
4.2 INDUSTRIAL R&D SCENARIO
* Patent Innovation Point Mining: In the process of patent application, use the model to mine technological innovation points. Through comparative analysis of existing technologies and R&D results, unique innovations are identified. For example, in the research and development of a new electronic
product, the model helped the R&D team to dig out a number of innovative points from the technical principles and functional implementation, which provided strong support for the patent application.
* Technical Solution Verification Tree: Build a technical solution verification tree to analyze and verify the feasibility of different technical solutions. The model can simulate the implementation effect of technical solutions under various conditions and evaluate their advantages and
disadvantages. For example, in the R&D of automobile engines, different combustion technology schemes are simulated and verified through the technical scheme verification tree, and the optimal scheme is selected for further research and development.
* Experimental Data Correlation Analysis: Perform correlation analysis on experimental data to find out the relationship between different factors. In chemical experiments, the model can analyze the influence of reactant concentration, temperature, reaction time and other factors on the
experimental results, and provide guidance for experimental optimization. Practical case: A chemical company optimized the production process and improved product quality and production efficiency through experimental data correlation analysis.
4.3 EDUCATION AND RESEARCH SCENARIOS
* Literature Review Generation Framework: Provide a literature review generation framework to guide the model to integrate relevant literature and generate coherent review content. For example, input the research topic and a list of relevant literature, and the model generates a literature review
according to the framework structure such as introduction, research status, research shortcomings, and future prospects. Case study: A graduate student used the framework to quickly generate a first draft of a literature review on AI algorithm research, saving a lot of time and effort.
* Experimental Design Optimization Path: Help optimize experimental design, and propose reasonable experimental variables, sample sizes, and experimental procedures based on research objectives and existing data. For example, in biological experiments, the model designs a reasonable experimental
group and control group, as well as the experimental operation process, according to the goal of studying gene function.
* Editing checklist: Develop a polishing checklist, including grammar checks, vocabulary richness, logical coherence, and more. The model can check and modify the paper point by point according to the checklist to improve the quality of the paper. Practical case: A researcher polished the first
draft of his paper through the model according to the checklist, and the language expression and logical structure of the paper were significantly improved.
V. EFFECTIVENESS EVALUATION SYSTEM (2000 WORDS)
5.1 QUALITY ASSESSMENT INDICATORS
* BLEU/ROUGE Optimization Direction: BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) are commonly used text generation quality evaluation indicators. In the optimization process, the lexical, grammatical and semantic similarity between the
generated text and the reference text is improved by improving the model training method and parameter adjustment. For example, in machine translation tasks, the model is continuously optimized to improve the BLEU score, bringing the translation results closer to the quality of human translation.
* Fact consistency verification: Checks the consistency of the generated content with known facts. For texts involving specific data, events, and other information, they are verified by comparing them with authoritative data sources. For example, when generating a news story, verify that the time,
place, and people of the event in the story are accurate.
* Logical self-consistency testing: Evaluates the logical coherence and plausibility of the generated text. Check the text for inconsistencies, unreasonable causal relationships, and other issues. For example, when generating a story, make sure that the storyline develops logically and that the
characters' actions and motivations are reasonable.
5.2 COST CONTROL MODEL
* Token Economics Analysis: Analyze the usage of tokens to understand the relationship between the number of tokens consumed by the model in the process of generating text and the quality of the generation. By optimizing the input prompts and model parameters, the consumption of tokens is minimized
on the premise of ensuring the quality of generation. For example, by streamlining the input text and reasonably setting the length of the generated text, the amount of token usage can be reduced, thereby reducing the cost of use. Case study: When a content creation team uses DeepSeekAI to
generate articles, it adjusts the input strategy through the analysis of the token consumption of articles on different topics, and reduces the token consumption by [X]% without affecting the quality of the articles, saving costs.
* Batch Processing Optimization: For tasks that require large amounts of data to be processed, batch processing can improve efficiency and reduce costs. Combine multiple requests into a single batch request and send it to the model, reducing the number of launches and communication overhead of the
model. For example, when generating or optimizing a batch of product descriptions, all product information is organized into a batch task and submitted to the model for processing at once. Practical case: An e-commerce company needs to generate a large number of product descriptions every day,
and through batch processing optimization schemes, the processing efficiency is increased by [X] times, and the cost is reduced by [X]%.
* Cache Policy Design: Design a reasonable caching strategy to cache frequently used or generated results. When the same request is encountered again, the result is fetched directly from the cache to avoid double counting. For example, in an intelligent customer service system, answers to
frequently asked questions are cached, and when a new user asks the same question, the answer is quickly returned from the cache, improving the response speed and reducing the number of model calls. Practical case: After the Q&A system of an online education platform adopts the caching strategy,
about [X]% of the answers to frequently asked questions are directly obtained from the cache, which shortens the system response time by [X]% and reduces the cost of using the model.
5.3 SECURITY COMPLIANCE FRAMEWORK
* Data masking standards: Establish strict data masking standards to ensure that sensitive information is properly protected during data processing and use. Sensitive data such as personal identity information (such as names, ID numbers, and financial information) are desensitized by means of
substitution, masking, and encryption. For example, the middle digits of the ID number are replaced with asterisks, and the phone number is partially digitally masked. Practical case: In the medical data processing project, the patient's medical record data is processed according to the data
desensitization standard, which effectively protects the patient's privacy while ensuring the availability of the data.
* Ethical review process: Establish an ethical review process to conduct ethical evaluation of the application scenarios and generated content of the model. Ensure that the model does not produce discriminatory, harmful, or unethical content. For example, in a recruitment screening system, review
the model for unfair screening results based on gender, ethnicity, and other factors. Practical case: When a social media platform used DeepSeekAI for content review, it discovered and corrected the bias of the model against some specific groups in a timely manner through the ethical review
process, and maintained the fairness and good image of the platform.
* Intellectual Property Protection: Strengthen intellectual property protection measures to clarify the copyright ownership and use rights of model-generated content. For content generated based on user input, ensure that users have legitimate rights and interests in use; For the intellectual
property rights of the model itself, prevent unauthorized use and copying. For example, when using a model in cooperation with a business, the rights and obligations of both parties in terms of intellectual property rights are clarified through a contract. Practical case: In the process of
developing software with DeepSeekAI, a software development company strictly abides by the provisions of intellectual property protection and signs a detailed contract with the model provider, which protects the legitimate rights and interests of both parties and avoids potential legal disputes.
6. VERSION ITERATION CHANGE LOG
[VERSION 1.0] - [RELEASE DATE]
* Core Feature Release: DeepSeekAI is officially launched, with basic text generation, data analysis and processing, and code generation capabilities.
* Technical architecture construction: The technical architecture is built based on the advanced Transformer model to achieve the initial integration of multimodal capabilities.
* Performance Parameter Setting: Determines the initial context window size, response delay optimization strategy, and multi-turn dialogue attenuation control mechanism.
[VERSION 1.1] - [RELEASE DATE]
* Enhancements: Optimized the text generation capability matrix with significant improvements in creative, technical, and academic text generation; Added code generation support for more programming languages.
* Precision Input Optimization: Introducing the CRISP framework in structured prompt engineering to improve the accuracy and validity of inputs.
* Performance Improvements: Optimized the contextual window management strategy, expanded the window size, and improved the processing capacity of long texts.
[VERSION 1.2] - [RELEASE DATE]
* Advanced Feature Expansion: Launched API deep integration capabilities, including asynchronous calls and stream response processing, to improve system performance and user experience.
* Parameter Fine-tuning and Optimization: The parameter fine-tuning guide has been improved, and detailed descriptions and practical cases of Temperature dynamic tuning strategies and top-p sampling optimization curves have been added.
* Security and compliance upgrades: Strengthen data desensitization standards and intellectual property protection measures to ensure the security of user data and legitimate rights and interests.
[VERSION 1.3] - [RELEASE DATE]
* Industry Application Deepening: Add more practical cases and practical templates to industry application scenarios such as financial risk control, industrial R&D, and education and scientific research, such as financial report analysis prompt templates and patent innovation point mining tools.
* Improvement of performance evaluation: Refine the quality evaluation indicators, and add specific methods and tools for factual consistency verification and logical self-consistency testing; The cost control model was optimized, and the batch processing optimization scheme and caching strategy
design were proposed.
* Multimodal Capability Improvement: Improved image annotation best practices and tabular data cleaning standards to further optimize multimodal input.
[VERSION 1.4] - [RELEASE DATE]
* Privatization Deployment Optimization: Updated the hardware resource configuration matrix to provide more detailed domain model fine-tuning processes and security audit checklists to meet the needs of enterprise privatization deployment.
* User Feedback Optimization: Based on user feedback, fix some known issues, optimize the performance of the model in specific scenarios, and improve the quality and stability of the generated content.
* Ease of Use Improvements: Simplify the operation process of some complex functions, provide more visual operation guides, and lower the threshold for users.
7. TROUBLESHOOTING MANUAL
CONNECTIVITY ISSUES
* Symptom: Unable to connect to the DeepSeekAI service.
* Possible causes: Network faults, incorrect API keys, or server-side maintenance.
* Solution: Check whether the network connection is normal; Confirm whether the API key is filled in correctly, and try to obtain the key again. Check whether there is a server-side maintenance notice in the official channel, and wait for the maintenance to end before trying to connect.
THE BUILD RESULTS ARE NOT AS EXPECTED
* Phenomenon: The generated text content is of poor quality, inconsistent with the input intent, and has logical errors.
* Possible causes: The input prompt is not clear, the model parameter settings are unreasonable, and the training data is biased.
* Solution: Optimize input prompts and clarify context, role, intent and other information according to the structured prompt engineering method; Adjust model parameters, such as Temperature, Top-p, etc., to find the appropriate combination of parameters according to the requirements of the task;
If the problem persists, consider feeding back to the official team, which may need to optimize the training data.
PERFORMANCE ISSUES
* Phenomenon: The response delay is too long and the processing speed is slow.
* Possible causes: Insufficient hardware resources, too many concurrent requests, and too high model load.
* Solution: For private deployment users, check whether the hardware resource allocation meets the requirements, and consider upgrading the hardware. If there are too many concurrent requests, adjust the request frequency or use asynchronous calls. Pay attention to the official platform status, and
if the performance problem is caused by the high load of the model, wait for the load to decrease before trying to operate.
SECURITY ISSUES
* Phenomenon: Risk of data breach, security warning.
* Possible causes: Data masking is not properly implemented, vulnerabilities exist in the security audit process, or the system is attacked.
* Solution: Check the implementation of the data masking standard and re-desensitize sensitive data. Conduct a comprehensive self-check according to the security audit checklist to fix the vulnerabilities found; If you suspect that the system has been attacked, immediately stop the relevant
operations, contact the professional security team for investigation and handling, and notify the official platform in time.
COMPATIBILITY ISSUES
* Phenomenon: Does not work properly in a specific operating system, browser, or application environment.
* Possible cause: The software version is incompatible and the dependencies are missing.
* Solution: Confirm the requirements of the operating system, browser version, and other environment requirements supported by DeepSeekAI, and upgrade or replace to a compatible version; Check if the necessary dependencies are missing, install and configure them according to the official
documentation.
8. VISUAL OPERATION FLOW CHART
OVERALL USAGE PROCESS
* Start: The user opens the DeepSeekAI app interface or calls the API.
* Input stage: According to the task requirements, the user fills in the relevant information in the interface input box or API request, including text descriptions, images, table data, etc., and sets model parameters according to the precise input methodology.
* Processing Stage: After receiving the input request, the system invokes the corresponding model for processing according to the functions selected by the user (such as text generation, data analysis, code generation, etc.). During the processing process, advanced functions such as deep API
integration and parameter fine-tuning may be involved.
* Output Phase: After the model is processed, the generated results are returned to the user. Users can view the results in the interface or get the result data through the API.
* Evaluation and Feedback: Users conduct quality evaluation, cost analysis, etc. on the generated results according to the performance evaluation system. If you are not satisfied with the results or find a problem, you can solve the problem according to the troubleshooting manual or give feedback
to the official team for further optimization.
THE OPERATION PROCESS OF EACH FUNCTIONAL MODULE
* Text Generation Function: The user enters the text generation interface and selects the generation mode such as creative, technical or academic; Enter the text prompt information, set the parameters such as the length and tone of the generated text; Click the generate button to generate the text
and display it to the user. Users can polish the generated text according to their needs, modify the parameters, and generate it again.
* Data analysis and processing function: users upload structured or unstructured data files, or directly enter data on the interface; Select the type of data analysis task (such as data cleaning, statistical analysis, sentiment analysis, etc.); Set relevant parameters, such as data format and
analysis dimensions. The system performs data analysis and processing, and displays the results in the form of charts and reports.
* Code Generation Function: The user enters the code to generate a natural language description of the requirement and selects the target programming language; Set the relevant parameters of code generation, such as code style, functional complexity, etc.; The model generates code snippets and
presents them to the user; Users can compile, debug and other operations on the generated code, and adjust the input to regenerate if there is a problem.
