Course · 5 chapters
Evaluación de LLMs
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
- 1Evaluación de LLMs: Empieza Aquí
Una orientación de 12 minutos al skill path de Evaluación de LLMs — el capítulo de acceso, luego las tres capas (judges, suites, gates) que convierten el eval-por-intuición en una disciplina que se puede lanzar.
- 2Fundamentos de evals: tu primera eval de LLM en 30 minutos
Deja de revisar outputs por intuición. Construye una eval ejecutable — dataset dorado, scorer determinista, juez LLM — y lee el resultado como un ingeniero.
- 3LLM-as-Judge: Rúbricas, Sesgos y Confiabilidad
Diseña jueces que sobrevivan los sesgos CALM, se calibren contra humanos y ganen un lugar en tu gate de CI.
- 4Inspect AI: Suites de Eval de Producción a Escala
Crea, ejecuta y visualiza suites de eval de nivel frontera con el framework open-source de UK AISI.
- 5Eval Gating en CI: Bloquear Merges Malos
Cablear evals por-PR en GitHub Actions, elegir umbrales que sobreviven al flakiness, y decidir cuándo un gate pertenece en 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.