Let AI work for you? GPT Improves Development Efficiency Guide


Teng Xiaoyun guide

In the entire daily workflow of developers, what can AI large models do? Can large AI models such as ChatGPT complete the entire process from technical solution output, coding, testing, release to operation and maintenance step by step through the guidance of developers? What are the pitfalls in use? This article starts from the various links of the actual R&D process, and summarizes and shares the practice of improving the R&D efficiency of the AI big model. Welcome to watch~

Table of contents

1 Demand Analysis

2 Technical solutions

3 coding

4 tests

5 releases

6 Operation

7 Precautions for Developers Using AI Large Models

8 summary

01. Demand analysis

· Extract key points of requirements

ChatGPT automatically extracts key requirements and function points through the analysis of requirements documents, so that the development team can better understand project requirements.
· Draw a flowchart

By summarizing and summarizing the content of the demand list, the demand list can be converted into a flowchart for easy understanding.


actor user
actor initiator
actor participant
box "Pinduoduo platform" #LightBlue
participant "Product Details Page" as G
participant "group initiation/participation" as P
participant "Payment Page" as Pay
participant "Order Confirmation" as O
participant "group success/failure" as S
end box

User -> G : View product details

G -> P : choose to start a group or participate in other people's group

P -> Pay : Jump to the payment page

Pay -> O : The payment is successful and the order is generated

O -> S : Group success or failure

note over P : Initiate a group fight: Create a new group\
Participate in a group fight

note over S : Successful group joining: the number of people has reached the requirement\
Failed to join the group: the number of people has not reached the timeout requirement



02, technical solutions

· Large table update scheme

Take a certain scenario I personally experienced as an example: In a certain payment business, there is a large table with 40 million rows of data. The version of mysqlA5.6 used needs to update the data recorded in a certain row. Let ChatGPT design a large table update plan for mysql , and analyze the risk of deadlock.

GPT provides a plan for updating in batches, and gives specific sql, and reminds users to back up data before starting.


·Research on industry solutions

If you want to implement a specific system, you can ask about solutions in the industry through ChatGPT.


·Ask for design details

In terms of specific implementation details, you can also ask ChatGPT to give suggestions.

· Read English documents or papers

When you encounter documents that you don’t understand well, you can ask AI to help translate and summarize them.



03, Encoding

· Generate Code (GitHub Copilot)

Enter a note and wait for the suggestion.


The local code can be obtained and code hints can be performed.

· Generate unit tests (GitHub Copilot)

Enter comments and wait for unit tests to be generated.

// unit test of XXX function


· Generate Documentation (GitHub Copilot)
Add // before the code that needs to generate comments

· Build Command (GitHub Copilot)

GitHub copilot can automatically generate corresponding command line instructions or answers based on natural language instructions or questions input by users.

For example, the user can enter “install react” or “how do I run this file”, etc., and github copilot will generate appropriate command line instructions or answers based on the user’s input and context.


· Language Transformation (ChatGPT)


Convert python code to C++ code.

· Interpretation Code (ChatGPT)

Explain the code and draw a flowchart.


·Restore obfuscated code (ChatGPT)


04, test

· Automatically generate test cases and test scripts

According to requirements and code logic, corresponding test cases and test scripts are automatically generated to improve test efficiency.

· Performance testing and optimization recommendations

Perform performance tests on the code, and give optimization suggestions to improve system performance.


· Security Vulnerability Analysis


Image source https://www.secpulse.com/archives/198731.html

05, Release

· Deployment script generation

ChatGPT can automatically generate deployment scripts according to the technology stack and deployment environment of the project, helping the team to complete the deployment work more quickly.


· Deployment problem diagnosis and resolution

When encountering problems during the deployment process, ChatGPT can assist in diagnosing the cause of the problem and provide corresponding solutions.


06. Operation

· Fault diagnosis and solution

In the event of a failure, ChatGPT can assist the team in diagnosing the cause of the problem and provide corresponding solutions to quickly restore services.


· User Feedback Analysis

ChatGPT can analyze user feedback data to help the team understand user needs and pain points, thereby optimizing products and services.


07. Notes for developers using AI large models

7.1 Accuracy Let’s go straight to an intuitive example:

Possible causes:

Limitations of training data: ChatGPT is trained on a large amount of text data. However, these data may contain misinformation, out-of-date information or inaccurate opinions. Therefore, the model may have learned these wrong knowledge during the training process.

Comprehension of the model: While ChatGPT has strong capabilities in natural language processing, it does not have real comprehension. Sometimes the model may misinterpret the user’s question or context, giving wrong or irrelevant answers.

Model generation ability: When ChatGPT generates answers, it may infer based on the probability distribution in its training data. As a result, it may sometimes generate more popular but incorrect answers while ignoring more accurate but less frequent answers.

Confidence of the model: When ChatGPT generates an answer, it may not be able to accurately assess the reliability of the answer. When faced with complex or ambiguous questions, the model may not be able to give a clear answer, but instead generate a relatively reasonable answer based on its training data.

Ambiguity of question formulation: If the user’s question formulation is unclear or ambiguous, ChatGPT may have difficulty accurately grasping the intent of the question, thus giving wrong or irrelevant answers.

7.2 Hallucinations

Using chatGPT requires special attention to its no-nonsense nonsense:



When designing logs and monitoring, the following factors should be considered:

First, ChatGPT may have inherent biases or limitations in its training data, which may not cover all possible situations or domains. Therefore, when the input text is complex or ambiguous, it may generate output based on incomplete or inaccurate information.

Second, the company where ChatGPT is located has set up a “content filter” to prevent it from producing inappropriate or harmful output. However, these filters may not be perfect, they may sometimes filter out some normal or useful output, or they may be bypassed by some tricks. One such technique is called “hypnosis,” and it involves adding some suggestive or introductory sentences to the input text to change ChatGPT’s output range and reward mechanism.

Third, ChatGPT may not have a reliable way to verify the consistency of its output with reality or external sources. As such, it may generate output that is inconsistent with facts or common sense, or that contradicts its previous answers.

7.3 Timeliness

The data of ChatGPT is only available until September 2021, so you need to pay attention to the time when the data is asked.

7.4 Intellectual Property

Intellectual property ownership of AI-generated content remains a murky and evolving field in many countries. Regarding the determination of intellectual property rights of AI-generated content, there is currently no unified international standard. Laws may deal with this issue differently in different countries.

Generally speaking, the attribution of intellectual property rights may be affected by the following factors:

The degree of creativity of AI: In some countries and regions, if the AI system only assists human creators to complete works, then the intellectual property rights may belong to human creators. However, if the AI system has a high degree of creativity, the content it generates may involve complex issues of intellectual property ownership.

Level of human involvement: In some cases, the level of human involvement may affect IP attribution. For example, if a human creator has substantially edited and adapted AI-generated content, then the intellectual property rights may belong to the human creator.

Applicable laws and jurisprudence: The laws and jurisprudence of different countries and regions may have different ownership of intellectual property rights for AI-generated content. For example, intellectual property laws in the European Union and the United States often require works to have a human creator, while laws in countries such as the United Kingdom and Australia deal with the issue more leniently.

User agreements and contracts: When using an AI system, users may be required to sign an agreement or contract, which may contain provisions regarding ownership of intellectual property. These agreements or contracts may stipulate that the intellectual property rights of the generated content belong to the developers, users or other relevant parties of the AI system.

Please note that the above information is for reference only, and specific intellectual property ownership issues may vary from case to case. When dealing with intellectual property issues related to AI-generated content, please consult a professional lawyer or compliance advisor.

7.5 Security Privacy and Compliance

It needs to comply with data privacy protection, content review and other institutional norms.

08. Summary

Using ChatGPT can quickly help developers complete the entire product workflow and improve work efficiency, but its capabilities in terms of accuracy and security are still open to question. Therefore, developers should pay special attention to data privacy and intellectual property issues while using ChatGPT to complete their work. The above is the whole content of this article, if you find it useful, please like it~


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