The AI Eval Playbook.
How to ship LLM features you can actually trust. The eval-first workflow we use on every AI engagement — build the harness before the first prompt, and never guess whether a change made things better again.
Engineering teams putting LLMs into production — RAG, agents, tool-use — who are tired of “it feels better” and want a measurable way to ship and improve.
What you'll learn
- 01Write evals first — how to define “good” before you write a single prompt, and why it changes everything.
- 02Build the golden set — curating the 50–200 cases that catch real regressions without slowing you down.
- 03Grade at scale — when to use exact-match, when to use an LLM judge, and how to keep the judge honest.
- 04Catch RAG drift — the retrieval metrics that matter once your corpus grows past the demo.
- 05Evals in CI — gate every deploy on a pass rate, so quality stops being a vibe and becomes a number.
From “feels good” to a pass rate.
Each part ends with something you can run today — a template, a metric, or a snippet you drop straight into your pipeline.
Define the harness
Pick your grading method, scope the golden set, and write the first ten evals before any prompt engineering begins.
Judge without lying to yourself
LLM-as-judge done right — rubric design, bias checks, and the calibration step almost everyone skips.
Wire it into CI
Gate deploys on pass rate, track drift over time, and alert before your users notice the model got worse.
The harness fits on one screen.
The playbook ships with a runnable starter, not pseudocode. This is the core loop — cases in, graded results out — that everything else builds on.
// the whole eval loop — cases in, score out
for (const c of goldenSet) {
const out = await model(c.input);
const pass = await grade(out, c.expected, {
method: "llm-judge",
rubric: c.rubric,
});
results.push({ id: c.id, pass });
}
const rate = passRate(results);
// gate the deploy on it
if (rate < 0.95) fail(`evals dropped to ${rate}`);Want us to build the eval layer with you?
The playbook is the method we use on every AI engagement. If you'd rather have senior engineers stand up the harness alongside your team, that's the work we do.