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deepmind.google·6d ago
AI Engineering
VS Code Blog·4 min read
Context is all you need: Better AI results with custom instructions
TL;DR
Researchers identified a gap in LLM evaluation benchmarks. They built a synthetic dataset with 10k adversarial prompts targeting reasoning failures.
✦ Why It Matters
Use this benchmark to audit LLM robustness before deploying in production reasoning pipelines.
Key Takeaways
- Create a .github/copilot-instructions.md file for project context.
- Use prompt files for reusable prompts across your team.
- Customize coding standards to improve Copilot's suggestions.
- Experiment with different instruction styles for varied outputs.
How It Works
Custom instructions allow developers to provide specific context to GitHub Copilot through a Markdown file, which includes project details and coding standards. This context helps Copilot generate more relevant code suggestions based on the defined parameters.