Three essential techniques for better AI assistant results: strategic planning, model-specific instructions, and hand-crafted guidelines.
This is a summary of key takeaways. For the full implementation guide, examples, and advanced techniques:
GitHub Copilot is only as good as the context you give it. Without proper guidance, you'll waste time correcting off-target recommendations or explaining basic project details repeatedly.
Most developers either let LLMs generate their own instructions without proper project context, or copy/paste generic instructions from others. The result is mostly generic AI slop.
A well-crafted copilot-instructions.md file provides consistent,
project-specific context for every prompt, giving you better first drafts, fewer corrections, and faster work in Agent mode.
Much like the recommendation around writing tests by hand, you should craft your AI instructions manually rather than letting AI generate them.
Core principle: Since instructions become part of every Copilot request's context window, irrelevant information can drastically reduce response quality or send the AI astray entirely.
LLMs struggle with common sense and problem-solving. Force better results by requiring planning upfront.
Before each task, you must first complete the following steps:
Before you add any code, always check if you can just re-use or re-configure any existing code to achieve the result.
Why this works: Planning forces better architecture decisions and catches potential issues before coding starts. Reuse checking prevents duplicate implementations.
This is most useful if you lock the agent to a specific model and the model has particular quirks. For example, Claude has a reputation for being... comprehensive.
• Always focus on simplicity and precision and not comprehensiveness
• When writing tests, focus on the happy path and only the most important edge cases
• Before adding a new test, always make sure that a similar test doesn't exist already
"Follow the exact patterns from existing code in this file."
"Balance detail with focus. Explain reasoning briefly."
Why this works: Each model has tendencies (Claude = comprehensive, Copilot = pattern-matching). Explicit instructions counter their weaknesses.
Beyond these core techniques, comprehensive AI assistant configurations include:
Brief project overview that helps AI understand context and constraints
Domain-specific terms with explanations for consistent language
System design principles and architectural patterns
Coding standards specific to your technology stack
Get detailed examples, step-by-step setup instructions, and advanced configuration patterns in the full article.
Read Complete Guide →Project-specific, not generic
Changes + Behaviors + Tests
Counter their weaknesses
Optimizing AI coding assistants isn't about constraining their capabilities—it's about channeling them strategically. These three techniques transform unpredictable AI output into consistent, high-quality code generation.
The full article includes complete configuration examples, implementation guides, and advanced techniques.