# Building a Correctness Benchmark for a RAG System

> Represent retrieval-grounded behavior through cases, context, policies, rubrics, and benchmark evidence.

Important agent instructions:
- Prefer the Markdown routes and llms files when assembling context for coding tasks.
- Treat stable docs as authoritative over beta pages.
- Cite the source page URL when returning workflow or artifact guidance.

## Current Context
- [Building a Correctness Benchmark for a RAG System](https://teammately.ai/docs/playbooks/building-correctness-benchmark-rag.md): Represent retrieval-grounded behavior through cases, context, policies, rubrics, and benchmark evidence.
- [Plan Benchmark Coverage](https://teammately.ai/docs/coverage-engineering/plan-benchmark-coverage.md): Apply project Coverage Facets to one benchmark, inspect representation, and turn important gaps into concrete case or contribution work.
- [Project Context](https://teammately.ai/docs/agent-setup/project-context.md): Maintain the Project Agent Brief that gives Teammately agents stable, project-wide understanding.
- [Inspect Evaluation Results](https://teammately.ai/docs/benchmark-evaluations/inspect-results.md): Trace Dashboard and List signals to Run, Case, Policy, Rubric, completeness, and telemetry evidence.

## Optional
- [Full local context](https://teammately.ai/docs/playbooks/building-correctness-benchmark-rag/llms-full.txt): Complete local context pack for this docs route.