6 lessons
Why evaluation is part of context engineering
Every technique in this course so far was a guess about what improves quality — evaluation is how you find out whether that guess was actually right.
Building a golden evaluation set
The single most valuable asset for evaluating a context-engineered system — where good test cases come from, how many you need, and how to keep the set honest over time.
Retrieval metrics: precision, recall, MRR, and nDCG
The full family of metrics for scoring whether retrieval actually found the right documents — from simple precision@k up through rank-aware metrics like MRR and nDCG.
LLM-as-judge and generation scoring
How to score a generated answer's quality when there's no single correct string to match against — faithfulness, relevance, and using a model to judge a model.
Scoring strategies for rerankers and agent trajectories
Evaluation doesn't stop at plain retrieval and generation — scoring a reranker's actual lift, and judging whether a multi-step agent took a good path, need their own strategies.
Continuous evaluation in production
Evaluation doesn't stop at launch — regression testing, online A/B testing, and monitoring for silent quality drift keep a context-engineered system honest long after it ships.