GitHub emphasizes that custom directives can significantly improve Copilot's contextual understanding and output quality. By infusing Copilot with team standards, project context, and toolchain, the AI can avoid detours and write less wasteful code, making code reviews and documentation production easier. 1. Why write custom directives for Copilot? 1. Customize the AI to fit your project and standards. Custom directives clearly define the project stack, naming conventions, coding style, and test boundaries. This allows the AI to generate implementable code and documentation based on your project constraints, rather than simply "generalizing output." 2. Build long-term AI preferences. By setting commonly used libraries, error blacklists, performance redlines, and other features in long-term memory, Copilot can reuse them in subsequent sessions, reducing re-explanation and rework.
（1）Reduce back-and-forth communication
Clarify input and output formats, examples, and forbidden words. AI will generate directly according to the template, significantly reducing review time.
（2）Cooperate with tool chain automation
Declare the test framework, static inspection, and packaging process in the instructions. AI will prioritize generating implementations and scripts that can pass the pipeline.
II. Five replicable writing skills
1. Define roles and boundaries before writing
Make it clear that "you are a senior engineer in a certain technology stack and can only call these types of libraries. Prioritize writing testable code." And explain the unallowed patterns and performance limits.
2. Condense the project context into a list
Organize the language version, dependencies, directory structure, and configuration points into three or five key points, allowing AI to obtain context at a glance rather than fragmented information.
（1）Structured output format
Requires a five-part return format including code, ideas, complexity, test cases, and potential risks to avoid just giving “answers without processes”.
（2）Example-driven and counterexample guardrail
Attach a good example and a counterexample to tell AI “write like this, don’t write like that”, and the effect will be immediate.
III. Engineering implementation and team collaboration
1. Put instructions into repositories and templates
Put custom instructions, contribution guidelines, and Lint rules in the same place, and reuse the same baseline across all repositories to reduce individual differences.
2. Establish an instruction iteration mechanism
Write back issues such as “misuse of libraries, performance degradation, and style drift” into the instructions, update them in small steps every week, and observe the pass rate and review density through small sample regression.
（1）Multiple versions for different scenarios
Differentiate between different instruction versions for “fixing bugs, writing features, writing documentation, and writing tests” and load them on demand to avoid “all at once”.
（2）Alignment with security and compliance
Declare license requirements, dependency whitelists, and confidential data principles in instructions to prevent AI from introducing non-compliant components or leaking information.
Frequently Asked Questions (Q&A)
Q: What is the difference between custom instructions and ordinary prompts (AI keywords: Copilot)?
A: Custom instructions are long-term “basic settings” used to constrain Copilot’s behavior; prompts are more like on-demand task instructions. The combination of the two can significantly improve stability.
Q: How does a team unify custom instructions (AI tool: GitHub Copilot)? A: Incorporate the instructions into the repository template and contribution guide, and implement linting, testing, and pre-commit hooks to ensure that the generated code is consistent with the pipeline. Q: How do you evaluate the effectiveness of the instructions (AI keyword: coding quality)? A: Create a regression set to observe the number of lines modified during review, the first-pass pass rate for tests, the number of static check warnings, and the build time, performing continuous A/B comparisons. Q: Will this make the AI "rigid" (AI keyword: style control)? A: Maintain the two "exploration mode" and "strict mode" instructions; allow for more leniency in early development exploration and switch back to strictness before release, balancing creativity and stability.
