You shipped a prompt tweak that looked better in three manual spot-checks, then watched a customer thread light up because it quietly regressed tool-calling on the exact edge cases you never re-tested. In 2026, “it worked when I tried it” is how LLM features rot in production: non-deterministic outputs, silent model deprecations, RAG pipelines that start hallucinating after a knowledge-base update, and agents that drift on multi-step trajectories — none of which your existing unit tests can see. Without a real eval harness, every deploy is a bet you can’t grade.
This is for developers and ML engineers who are already calling LLM APIs and want a repeatable, CI-enforced way to measure quality before merge. We assume you’re comfortable with the command line, Git-based CI, JSON/YAML config, and basic prompt engineering. Out of scope: teaching you Python or JavaScript from scratch, foundational model training or fine-tuning, and vendor-specific hosting setup — this is about evaluating and gating the LLM features you already build.
Automated evals are excellent at the things humans do badly at scale: catching regressions across hundreds of cases on every commit, tracking cost and latency drift, and flagging when a prompt change moves your pass rate. They are worse at nuance — an LLM-as-judge can be miscalibrated, over-reward confident wrong answers, or miss subtle factual errors. We’re explicit throughout about where a judge is trustworthy, where it isn’t, and where human review of golden datasets, judge calibration, and high-stakes failures is non-negotiable.
What This Guide Covers
- Why evals are the new unit tests — the mental model that turns “vibes” into a measurable, gate-able quality signal
- The core vocabulary — scorers, assertions, judges, datasets, and experiments, so the whole stack stops feeling like jargon
- Building golden datasets that actually predict production behavior instead of flattering your prompt
- Getting productive in Promptfoo fast — from first config to a running eval suite
- Assertion-based grading — deterministic checks that catch the failures you can define up front
- Writing and calibrating LLM-as-judge prompts so your automated grader agrees with your best human reviewer
- Tracking experiments and scorers in Braintrust to compare runs and spot drift over time
- A clear-eyed Promptfoo vs. Braintrust comparison — how to choose tooling for your team and stack
- Evaluating RAG — measuring faithfulness, groundedness, and hallucination instead of guessing
- Evaluating agents — scoring tool-call accuracy and full multi-step trajectories
- Choosing metrics that matter — balancing pass@k, factuality, latency, and cost against real goals
- Wiring evals into CI as regression gates that block a bad merge automatically
- Case studies, pitfalls, and anti-patterns drawn from evals that quietly lied to their owners
- What’s next — continuous evals, online scoring, and where the 2026 roadmap is heading
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