advanced~5h
Evaluation Pipeline Design
How to design a complete LLM evaluation pipeline: decomposing complex tasks into per-component evaluations, writing evaluation guidelines, choosing annotation methods and data, and wiring the pipeline into a CI/CD deployment gate.
2
Subtopics
1
Exercises
1
Projects
5
Quiz Qs
4
Flashcards
▶📚 Prerequisites(2)
🎓 Learning objectives
- •Decompose a complex LLM application into individually testable components
- •Write evaluation guidelines that produce high inter-annotator agreement
- •Choose between human annotation, AI-as-Judge, and automated metrics for each evaluation dimension
- •Design a test set that is representative, uncontaminated, and maintainable
- •Integrate eval into a CI/CD pipeline as a deployment quality gate
Evaluation Pipeline Design is a Pro topic
Sign in, then upgrade to Pro or Power to unlock this topic and the full AI Engineering curriculum.
📂 Subtopics
Related concepts
component decompositiontest set designannotation guidelinesinter-annotator agreementCohen's kappaeval-as-CIregression testingRAG evaluationretrieval metricsAI-as-Judgedeployment gates
Next to learn
model-selection-benchmarking →ai-as-judge →