Using AI Coding Assistants in Chinese: A Practical Guide for Software Development

1. What Problems Can an AI Coding Assistant Solve?

An AI coding assistant is a development tool that uses natural-language interaction to understand project files, analyze code structure, and assist with coding, code modification, explanations, testing, and documentation.

Its value is not limited to generating a few lines of code. More importantly, it helps reduce the distance between a requirement and a working implementation.

Common sources of inefficiency in software development include:

1. Spending too much time understanding legacy code

   Projects with limited documentation, inconsistent naming, or significant historical complexity often require developers to inspect files individually and trace dependencies manually. An AI coding assistant can summarize the directory structure, module responsibilities, and key dependencies, helping developers build a high-level understanding more quickly.

2. Handling large amounts of repetitive coding

   Tasks such as data transformation, basic API wrappers, form validation, logging, and test-case generation may not be technically difficult, but they can consume considerable time. By clearly describing inputs, outputs, and constraints, developers can ask the assistant to create an initial implementation and then review and adjust it.

3. Repeating experiments during troubleshooting

   When a program fails, an assistant can analyze the error message, call stack, and related code to suggest possible causes and verification steps. However, suggestions cannot replace actual testing. The final conclusion should always be based on reproducible experiments.

4. Keeping documentation synchronized with code

   An AI coding assistant can generate API descriptions, deployment instructions, change logs, and test reports based on the implementation, reducing the cost of documentation maintenance.

The correct positioning is therefore that an AI coding assistant is a collaboration tool for improving development efficiency, not an automated system that completely replaces engineering judgment.

2. Preparation Before Using an AI Coding Assistant

Before introducing an AI coding assistant into a project, organize the development environment and define the project boundaries. Better preparation generally leads to more reliable output.

2.1 Define the Project Structure

A well-organized project should normally separate source code, tests, configuration files, scripts, documentation, and build artifacts. The directory structure does not need to be complicated, but files with different responsibilities should be easy to locate.

You can first ask the assistant to:

• Summarize the main directories and their responsibilities;
• Identify the application entry point;
• Mark core business modules and shared components;
• Map dependencies between modules;
• Point out potentially duplicated logic or highly coupled areas;
• Identify sensitive information that may be present in configuration files.

The purpose of this step is not to modify code immediately, but to let the assistant understand the project first. Establishing context before implementation helps reduce accidental file changes and missed dependencies.

2.2 Prepare Project Conventions

Create a development guidelines document for the project. It may include:

• The programming language and version;
• Code formatting and naming conventions;
• Error-handling practices;
• Logging standards;
• Test-file organization;
• Checks that must be run before submitting changes;
• Directories or files that must not be modified;
• Rules for handling sensitive data.

The clearer the conventions, the more likely the generated code is to match team expectations. For projects with multiple contributors, you can also document module boundaries, interface design principles, and the database migration process.

2.3 Protect Sensitive Information

Do not submit the following information directly to an AI coding assistant:

• Real user identity information;
• Credentials, keys, and access tokens;
• Unreleased business data;
• Internal network addresses;
• Unpublished business plans;
• Materials restricted by law or contract.

When necessary, sanitize the data first. For example, replace real names with placeholders, change production addresses to example addresses, and replace actual records with structurally equivalent mock data.

Security boundaries should be defined consistently by the team rather than relying only on individual habits.

3. How to Design High-Quality Prompts

The quality of an AI coding assistant’s output depends heavily on how completely the task is described. A useful prompt should contain more than “write some code.” It should provide background, objectives, constraints, and acceptance criteria.

3.1 A Recommended Prompt Structure

Organize prompts in the following order:

Background: Describe the project, module, and existing implementation.
Objective: Clearly state the feature or outcome required.
Inputs and outputs: Define data formats, parameter types, and return values.
Constraints: Specify what must not change, compatibility requirements, and performance boundaries.
Error handling: Describe failure scenarios and expected behavior.
Validation: Request tests, examples, or verification steps.

For example:

The current project contains a data-import module that reads structured files and converts them into internal objects. Add duplicate-record detection. Keep the existing public interface unchanged, use the unique identifier field as the detection rule, return clear error messages for malformed data, and add tests for valid data, duplicate data, and invalid data. First explain the implementation plan, then list the affected files, and finally generate the code.

This description is more effective than simply naming the desired feature because it provides context, boundaries, and acceptance criteria.

3.2 Use Staged Interaction

Complex tasks should not be assigned as a single, all-in-one request. A safer process is:

1. Analyze the requirement and existing code;
2. Present the implementation plan and risks;
3. Modify files only after the plan is confirmed;
4. Generate or adjust tests;
5. Continue fixing issues based on test results;
6. Summarize the changes and remaining considerations.

Staged interaction prevents the assistant from making large-scale changes based on a misunderstanding and gives developers opportunities to make decisions at critical points.

3.3 Ask the Assistant to State Its Assumptions

When a requirement is incomplete, explicitly ask the assistant to list its assumptions instead of making hidden decisions. For example:

• Is adding a dependency allowed?
• How should null or empty values be handled?
• Which time format should be used?
• Should failed API requests be retried?
• Is pagination required when the data volume increases?
• Must behavior from older versions remain unchanged?

If the assistant cannot determine the answer, ask it to raise questions first. In engineering work, clarification is often more valuable than immediate code generation.

4. Common Development Scenarios and Recommended Practices

4.1 Reading and Explaining Code

Ask the assistant to explain code at three levels:

• Functional level: What business problem does the code solve?
• Structural level: How do the functions, classes, or modules work together?
• Risk level: Are there edge cases, insufficient error handling, or resource leaks?

For complex functions, you can also request pseudocode, call flows, or data-flow descriptions. This can help new developers understand the system and provide a basis for refactoring.

4.2 Adding a Feature

When adding a feature, describe not only the desired result but also its relationship with the existing system. Ask the assistant to:

• Locate the relevant modules first;
• Reuse existing shared methods where possible;
• Preserve existing interface behavior;
• Add necessary input validation;
• Add tests for core logic;
• Explain which other features may be affected.

If multiple files are involved, ask the assistant to list the proposed changes before editing them. Review the list first, then authorize the changes. This can significantly reduce the risk of uncontrolled scope expansion.

4.3 Fixing Errors

When reporting an error, provide as much of the following information as possible:

• The complete error message;
• The steps that trigger the problem;
• The expected result;
• The actual result;
• Relevant code;
• Recent changes;
• Whether the issue can be reproduced consistently.

Ask the assistant to respond in the following order:

Observed behavior → Possible causes → Verification methods → Fix

After the fix, ask the assistant to explain why the change resolves the problem and to add regression tests.

4.4 Refactoring Code

Before refactoring, clarify whether the goal is to improve readability, reduce duplication, improve performance, or separate responsibilities. Different goals require different approaches.

Use incremental refactoring:

1. Add tests for the current behavior;
2. Make a local structural change;
3. Modify only one relatively independent area at a time;
4. Run formatting checks and tests;
5. Compare behavior before and after the change;
6. Move to the next issue only after verification.

Do not allow the assistant to rewrite a core module extensively without test coverage. The foundation of refactoring is verifiable behavior, not code that merely looks cleaner.

5. Testing, Review, and Result Validation

AI-generated code may contain syntax errors, missed edge cases, misunderstandings of interfaces, or security risks. Every change must therefore be validated.

5.1 Establish Multiple Levels of Testing

At a minimum, consider covering the following types of tests:

• Unit tests: Verify the independent behavior of a function or module;
• Integration tests: Verify that multiple modules work together correctly;
• API tests: Verify requests, responses, and error statuses;
• Boundary tests: Verify null values, extreme values, duplicates, and oversized input;
• Regression tests: Confirm that new changes do not break existing functionality.

You can ask the assistant to create a test matrix before generating test code. The matrix should include input conditions, expected results, and error scenarios rather than covering only one successful path.

5.2 Conduct Manual Code Review

During review, pay particular attention to:

• Unnecessary dependencies;
• Changes to public interfaces;
• Unhandled exceptions;
• Resources that may not be released;
• Missing authorization checks;
• User input concatenated directly into executable statements;
• Sensitive information exposed in logs;
• Inefficient loops or unnecessary data loading;
• Inconsistency with the project’s existing style.

For critical business logic and security-sensitive code, passing tests is not a reason to skip manual review.

5.3 Follow the Minimal-Change Principle

Ask the assistant to modify only what is necessary to complete the task. Avoid unrelated formatting changes, file renaming, or business-logic adjustments.

The smaller the change set, the easier it is to review, roll back, and troubleshoot.

6. Usage Guidelines for Team Collaboration

In a team environment, an AI coding assistant should be integrated into the existing development process rather than treated as an independent black box.

Consider establishing the following practices:

1. Define responsibility

   The developer who generates or submits the code remains responsible for its quality, correctness, and security.

2. Keep change records

   Larger changes should document the task background, modification scope, test results, and known risks.

3. Standardize prompt templates

   Teams can create templates for API development, issue investigation, test generation, and documentation writing to improve communication efficiency.

4. Limit the scope of automatic changes

   Core configuration, authorization modules, data structures, and production deployment files should be subject to stricter review procedures.

5. Create a feedback loop

   Track which tasks are suitable for the assistant and which tasks frequently produce errors. Use the findings to improve project conventions and prompt templates.

7. Common Mistakes

Mistake 1: Treating the Assistant as the Final Decision-Maker

An assistant can provide recommendations, but it cannot fully understand an organization’s processes, historical constraints, or unpublished context. Architecture, security, data migration, and production changes must receive final approval from experienced engineers.

Mistake 2: Focusing Only on Generation Speed

Fast code generation does not necessarily mean fast delivery. If the generated code later requires extensive debugging, additional tests, and risk mitigation, overall productivity may decrease.

The real goal should be to shorten the time required for reliable delivery.

Mistake 3: Expecting Accurate Results Without Enough Context

Without the project structure, interface conventions, and representative data, an assistant must rely on assumptions. The less specific the task description, the more likely the output is to diverge from the actual requirement.

Mistake 4: Ignoring Versions and Compatibility

Generated code may use syntax or dependencies that the current environment does not support. After generation, check the language version, runtime environment, build process, and deployment conditions. Request a compatible implementation when necessary.

8. A Recommended End-to-End Workflow

A reliable workflow can be summarized as:

Define the requirement → Provide context → Ask the assistant to analyze → Confirm the plan → Make small changes → Run automated tests → Conduct manual review → Perform integration validation → Record the changes

During requirements analysis, define the success criteria first. During the analysis stage, ask the assistant to list assumptions and risks. During implementation, follow the minimal-change principle. During validation, cover normal, abnormal, and boundary conditions. During delivery, retain clear change records.

Conclusion

AI coding assistants can significantly improve code reading, repetitive development, troubleshooting, and documentation work. However, their practical value depends on how they are used.

Effective practice does not mean delegating every task to an assistant. Developers should remain responsible for goals, constraints, and judgment, while the tool handles retrieval, organization, and initial implementation.

When a team establishes clear project conventions, practical prompt templates, rigorous testing procedures, and appropriate security boundaries, an AI coding assistant can become a reliable development partner. It can reduce repetitive work while allowing engineers to spend more time on architecture, business understanding, and quality assurance.