Peter Zhang
Jan 12, 2026 23:03
GitHub reveals three practical methods for developers to improve AI coding outputs through custom instructions, reusable prompts, and specialized agents.
GitHub is pushing developers to move beyond basic prompting with a new framework it calls context engineering—a systematic approach to feeding AI coding assistants the right information at the right time. The guidance, published January 12, 2026, outlines three specific techniques for getting better results from GitHub Copilot.
The concept represents what Braintrust CEO Ankur Goyal describes as bringing “the right information (in the right format) to the LLM.” It’s less about clever phrasing and more about structured data delivery.
Three Techniques That Actually Work
Harald Kirschner, principal product manager at Microsoft with deep VS Code and Copilot expertise, laid out the approach at GitHub Universe last fall. The three methods:
Custom instructions let teams define coding conventions, naming standards, and documentation styles that Copilot follows automatically. These live in .github/copilot-instructions.md files or VS Code settings. Think: how React components should be structured, how errors get handled in Node services, or API documentation formatting rules.
Reusable prompts turn frequent tasks into standardized commands. Stored in .github/prompts/*.prompts.md, these can be triggered via slash commands like /create-react-form. Teams use them for code reviews, test generation, and project scaffolding—same execution every time.
Custom agents create specialized AI personas with defined responsibilities. An API design agent reviews interfaces. A security agent handles static analysis. A documentation agent rewrites comments. Each can include its own tools, constraints, and behavior models, with handoff capability between agents for complex workflows.
Why This Matters Now
Context engineering has gained significant traction across the AI industry throughout early 2026, with multiple enterprise-focused discussions emerging in the same week as GitHub’s guidance. The discipline addresses a fundamental limitation: LLMs perform dramatically better when given structured, relevant background information rather than raw queries.
Retrieval Augmented Generation (RAG), memory systems, and tool orchestration all fall under this umbrella. The goal isn’t just better code output—it’s reducing the back-and-forth prompting that kills developer flow.
For teams already using Copilot, the practical upside is consistency across repositories and faster onboarding. New developers inherit the context engineering setup rather than learning tribal knowledge about “how to prompt Copilot correctly.”
GitHub’s documentation includes setup guides for each technique, suggesting the company sees context engineering as a core competency for AI-assisted development going forward.
Image source: Shutterstock
Source: https://blockchain.news/news/github-copilot-context-engineering-techniques
