Course · 5 chapters
LLM-evaluatie
Build runnable LLM evals you can trust: golden datasets, deterministic scorers, calibrated LLM judges, Inspect AI suites, and CI gates. 5 chapters, advanced, for engineers.
What you'll be able to do
- Build a runnable LLM eval from scratch
- Design judge rubrics that resist bias
- Calibrate LLM judges against humans
- Run eval suites with Inspect AI
- Gate bad merges with CI evals
- Set thresholds that survive flaky judges
What's inside
- 1LLM-evaluatie: begin hier
Een oriëntatie van 12 minuten op het skill path LLM-evaluatie — het openingshoofdstuk, gevolgd door de drie lagen (judges, suites, gates) die eval-op-gevoel omzetten in een discipline die ook echt shipt.
- 2Eval Foundations: Je eerste LLM-eval in 30 minuten
Stop met outputs op gevoel beoordelen. Bouw een uitvoerbare eval — golden dataset, deterministische scorer, LLM-judge — en lees het resultaat als een engineer.
- 3LLM-as-Judge: Rubrics, Bias en Betrouwbaarheid
Ontwerp judges die CALM-biases overleven, kalibreer ze tegen mensen, en geef ze een plek in je CI-gate.
- 4Inspect AI: Production Eval Suites op schaal
Schrijf, draai en visualiseer eval suites op frontier-niveau met het open-source framework van UK AISI.
- 5Eval-gating in CI: slechte merges blokkeren
Koppel per-PR-evals aan GitHub Actions, kies drempels die bestand zijn tegen flakiness, en bepaal wanneer een gate thuishoort op main.
Frequently asked questions
- What will I learn in this LLM evaluation course?
- You build evaluation across three layers: a first runnable eval with a golden dataset and scorer, reliable LLM-as-judge rubrics calibrated against human ratings, and eval suites wired into CI as a merge gate. The path uses UK AISI's open-source Inspect AI framework and GitHub Actions.
- Who is this course for?
- It is for engineers building production AI features who need to test LLM outputs rigorously instead of checking them by vibes. The level is advanced, with a focus on software engineering and AI reliability.
- Do I need to code to take this course?
- Yes. This is a hands-on engineering path that involves writing eval scripts, configuring the Inspect AI framework, and setting up GitHub Actions workflows, so comfort with code and CI is expected.
- How long is the course and is there a certificate?
- The path has 5 chapters totaling about 100 minutes, starting with a 12-minute orientation. On completion you earn an AI Academy by Anthropos certificate.
- Is this course free?
- No, this is a paid skill path included with an AI Academy by Anthropos subscription.
Earn a certificate
Complete all chapters to receive your certificate of completion.