← AI Agents & RAG learning pathAI Agents & RAG · Module 8 of 10
Evaluation and observability
Measure and monitor agent and RAG quality.
📖 Key ideas
- Build an evaluation set of real questions with expected answers.
- Automated graders (LLM-as-judge) plus human review.
- Trace every step: prompts, retrieved chunks, tool calls, outputs.
- Track cost, latency and failure rates.
- Re-run evals before every prompt or model change.
💡 Real-life example: Evals are mock exams for your AI app; tracing is a CCTV recording of each step it took.
Eval setTest questions with expected outcomes
LLM-as-judgeUsing a model to grade outputs
TraceRecord of each step in a run
RegressionQuality getting worse after a change