Evals for LLM Apps: Measure Before You Ship

Stop guessing whether your prompt, model, or agent got better. Build eval sets, metrics, calibrated LLM judges, and CI checks that tell you.

Most LLM features ship on vibes: someone tries five questions, it looks fine, it goes live. Then a model update or a prompt tweak breaks something nobody tested. This course gives you the habit that fixes it. You turn real user tasks into an eval set, pick metrics that match the job, build an LLM judge and check it against human labels, measure agents step by step with pass^k, and run everything in CI so every change gets a score. You finish with an eval suite for your own app and a one-page report you can show any team.

What you will be able to do

• Build an eval set of 20 to 50 real tasks for any LLM feature
• Choose metrics that match the task, and know when a judge is needed
• Calibrate an LLM judge against human labels before trusting it
• Block regressions in CI and compare models on cost, quality, and latency

Before you start

• Python basics
• You have built or used one LLM feature

Course outline

1. Why vibes fail: A list of the ways your feature can fail. A failure map for one real feature.
2. Build the eval set: 20 to 50 real tasks with expected results. A versioned eval set in your repo.
3. Pick the right metric: Exact, fuzzy, and rubric scoring. A scoring script that runs in minutes.
4. LLM as a judge: A rubric judge checked against your own labels. An agreement score you can quote.
5. RAG and agent evals: Retrieval, grounding, and trajectory checks with pass^k. A report per component, not one number.
6. Evals in CI and production: A CI job that blocks regressions, plus live sampling. A one-page eval report for your app.

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