Your eval suite says 94% pass and your users say the bot is worse than last week. Both are true. In 2026, “vibes plus a spreadsheet of 30 prompts” stopped surviving contact with reality: model providers ship silent point-release updates, your RAG index drifts weekly, and a prompt tweak that fixes one customer segment quietly breaks three others. Meanwhile the number you’re reporting to your team has no confidence interval attached, so you genuinely cannot tell whether 94% → 91% is a regression or noise from a 50-sample run. You end up shipping on hope, rolling back on Twitter complaints, and rewriting the same throwaway eval script for the fourth time because the last one didn’t log enough to debug anything.
This is for developers and ML engineers who already ship LLM features to production and are done with ad-hoc scripts — you write Python comfortably, you know your way around a virtualenv and a CI config, and you’ve at least held an API key. It assumes no prior evals experience and no statistics background beyond high-school arithmetic; the math you need is derived from scratch and reduced to formulas you can apply. Out of scope: model training and fine-tuning, RLHF, building your own inference stack, and general prompt-engineering theory. This is about measurement infrastructure — the harness that tells you whether your prompt engineering actually worked.
Straight talk on where AI helps and where it lies to you. Model-graded scoring is genuinely strong for open-ended, subjective criteria — tone, helpfulness, whether an answer is grounded in the retrieved context — and it scales to thousands of samples for pennies. It is bad at anything you can check deterministically (never ask a model to compare two numbers), it is biased toward longer and more confident answers, and it will happily grade its own output generously. Graders also drift when the underlying model updates, which means your measuring stick changes length without telling you. Human review is non-negotiable in three places: validating any new grader against a hand-labeled sample before you trust it, reading actual failure transcripts rather than only aggregate scores, and signing off on the pass/fail threshold that gates your deploys. The harness makes review cheap and targeted — it does not remove it.
What This Guide Covers
- A working mental model of Inspect AI’s three moving parts — task, solver, scorer — so you can read any eval in the ecosystem and know exactly where to intervene
- A clean install and a first passing eval running in about fifteen minutes, with the environment and key setup that trips most people up handled up front
- How to turn real production traces into an honest dataset that reflects what your users actually send, instead of the tidy examples you invented at your desk
- Solver patterns for chaining steps, templating prompts, and dropping in custom logic when the built-ins don’t fit your architecture
- A hard look at scoring: which checks belong in deterministic code, when a model-graded rubric earns its keep, and how to write rubrics that don’t reward waffle
- Evaluating the hard stuff — multi-turn conversations, tool-calling agents, and sandboxed execution where the model runs real code
- The statistics that make your numbers defensible: standard error, bootstrap confidence intervals, and how to compute the sample size you actually need before you run anything
- Failure triage with Inspect View and the log format — finding the twelve samples that explain your regression instead of scrolling through a thousand
- Cost discipline that lets you run thousands of samples on free and cheap tiers, including caching, batching, and where to spend your expensive-model budget
- A candid framework comparison against promptfoo, OpenAI Evals, and W&B Weave — what each is genuinely better at, and when Inspect AI is the wrong choice
- Wiring evals into CI/CD as real regression gates, including threshold selection that blocks bad merges without crying wolf on every PR
- The silent corruptors — subtle mistakes that leave your suite green while your measurements mean nothing, and how to detect each one
- A complete worked case study taking a RAG support bot from no evals at all to a passing, enforced quality gate
- Reusable structure you can lift directly into your own repo, so the second eval you build takes an afternoon instead of a sprint
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