🧪 Evaluating & Testing LLMs
Learn Evaluating & Testing LLMs on AI4AI — short, hands-on lessons with live AI runs, at three reading levels (beginner to expert). Free to start.
What you'll learn
- Why Evals Matter: Measure Before You Improve (7 min) — ⚡ An 'eval' (evaluation) is a repeatable, scored test that measures how well an LLM performs on a defined task. Without…
- Building a Test Set: Real Inputs & What Good Looks Like (7 min) — ⚡ A test set (also called an evaluation set or eval set) is a fixed collection of inputs paired with criteria or refere…
- Scoring Methods: Exact Match, Rubrics, and LLM-as-Judge (8 min) — ⚡ When evaluating LLM outputs, you need a scoring method that matches what 'correct' actually means for your task. Thre…
- User-Centric Metrics: Faithfulness, Helpfulness, Safety, Latency & Cost (8 min) — ⚡ When evaluating an LLM in production, five metrics cover what users actually experience — and all five must be tracke…
- Regression-Proof Your LLM: Evals in CI (8 min) — Continuous Integration (CI) is the practice of running automated checks every time code is merged. For LLM-powered prod…
- Offline to Online: A/B Tests, Live Feedback & Closing the Eval Loop (8 min) — Offline evals (running a fixed benchmark or human-labeled test set before deployment) catch obvious regressions but can…
Lessons
Why Evals Matter: Measure Before You Improve
⚡ An 'eval' (evaluation) is a repeatable, scored test that measures how well an LLM performs on a defined task. Without evals, the only feedback loop is human intuition — what practitioners call 'vibes.' Vibes do not scale: a single developer can eyeball 20 outputs, but not 20,0…
Building a Test Set: Real Inputs & What Good Looks Like
⚡ A test set (also called an evaluation set or eval set) is a fixed collection of inputs paired with criteria or reference answers used to measure model quality. The two hard parts are: (1) collecting inputs that reflect real usage, and (2) defining what a correct or good answer…
Scoring Methods: Exact Match, Rubrics, and LLM-as-Judge
⚡ When evaluating LLM outputs, you need a scoring method that matches what 'correct' actually means for your task. Three core approaches cover most cases. **Exact match** compares the model's output character-for-character (or token-for-token) against a reference answer. It's fa…
User-Centric Metrics: Faithfulness, Helpfulness, Safety, Latency & Cost
⚡ When evaluating an LLM in production, five metrics cover what users actually experience — and all five must be tracked together, because optimizing one often hurts another. **Faithfulness** (also called groundedness) measures whether the model's output is supported by its sour…
Regression-Proof Your LLM: Evals in CI
Continuous Integration (CI) is the practice of running automated checks every time code is merged. For LLM-powered products, 'code changes' include prompt edits, model version bumps, and retrieval-pipeline tweaks — all of which can silently degrade output quality without trigger…
Offline to Online: A/B Tests, Live Feedback & Closing the Eval Loop
Offline evals (running a fixed benchmark or human-labeled test set before deployment) catch obvious regressions but can't predict every real-world failure mode. The gap between benchmark performance and live user satisfaction is sometimes called the 'eval-to-production gap.' A/B…