Eval Fundamentals
The minimum eval loop, taught before RAG and agents on purpose: look at your data, build your first 50-example eval set, and wire assertions into CI. Nothing after this part ships without evals.
- Chapters
- 4
- Hours
- 1
- Difficulty
- Intermediate
- 4.0beginner
Why you can't ship without evals
The vibes-driven failure mode, evals as the AI engineer's CI, and the maturity ladder that gets you off the whack-a-mole loop.
- 4.1beginner
Look at your data
The single highest-ROI habit in LLM evaluation: read your traces by hand and build a failure taxonomy before automating anything.
- 4.2beginner
Your first eval set
Bootstrap a 50-example eval set in a week: real or realistic inputs, binary pass/fail labels, versioned in git, growing from production failures.
- 4.3beginner
Assertions and unit tests for LLM output
The cheap, deterministic checks that should gate every LLM commit before any judge model runs: schema, contains, regex, code-runs, SQL-parses, wired into pytest and CI.