Guided learning path
Evaluation & Benchmarking
Design measurements that distinguish speed, quality, retrieval effectiveness, and reliable agent execution.
Who this path is for
Engineers choosing between models or system designs, or defining release criteria for AI features.
What you will learn
Define a workload and success criteria before testing; choose useful baselines; measure component and end-to-end behaviour; connect failures to release decisions.
Prerequisites
Basic statistics, familiarity with an AI application, and comfort interpreting experimental results. Individual labs specify additional tooling.
Difficulty and workload
Intermediate. Start with benchmark design, then compare quality gates, retrieval baselines, and trajectory evaluation before considering production release controls.
Suggested reading sequence
- Benchmark LLM Serving Without Lying to Yourself
- When Q4 Passes Latency but Fails Quality
- Build a Lexical Search Baseline Before Adding Embeddings
- Evaluate the Agent Trajectory, Not Just Its Final Answer
- Evaluation Is the Control System for Production AI
This reading sequence uses existing articles. New guided lessons will appear in the published-lessons list below. Return to all learning paths to explore a related subject.
Curriculum
Published lessons
- No lessons have been published in this path yet.