/shelf/shared-resources

Retrieval and grounding evaluation kit

A compact resource pack for checking whether an AI system retrieves the right evidence before it answers.

Tags

• resources
• retrieval
• evaluation
• grounding
• reliability

This is the small stack I would hand to anyone trying to improve answer quality without getting trapped in prompt theater.

The common mistake is to review the generated sentence first. That usually hides the real problem. Weak evidence retrieval can still produce fluent output, which is why grounding and retrieval need their own checks.

Useful starting points:

• RAGAS for retrieval and answer evaluation patterns that separate faithfulness from surface quality.
• TruLens for practical feedback loops around retrieval, groundedness, and application traces.
• DeepEval for LLM evaluation workflows that can be adapted to retrieval and runtime checks.

The value of these resources is not the tooling itself. The value is that they force clearer questions: Did the system retrieve the right material? Did it stay grounded to that material? Did the final answer overclaim?

Related internal reading:

• AEO and GEO as a Retrieval Design Problem
• Evaluation as a Runtime Discipline
• Observability First: How AI Systems Learn After Launch