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deepmind.google·6d ago
AI Engineering
VS Code Blog·4 min read
The Coding Harness Behind GitHub Copilot in VS Code
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
- Understand how the coding harness integrates with language models.
- Utilize the Tools UI in Chat to explore available tools.
- Evaluate model performance using VSC-Bench for better outcomes.
- Experiment with different models to find the best fit for your workflow.
How It Works
The coding harness builds prompts that include user queries and workspace context, allowing the model to generate relevant outputs. It manages tool exposure by defining which actions the model can take, such as editing files or running commands.
The agent loop continuously refines the model's responses based on the latest context and tool results, ensuring coherent interactions.