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Evaluating LLM Applications
Module 2
Lesson 3 of 4
02.03 · Building datasets

Labeling with humans

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Course outline
Module 1
Why evals matter
4
  • 01.01The vibes-driven trap
  • 01.02Signal vs noise
  • 01.03What good looks like
  • 01.04Setting up an eval harness
Module 2
Building datasets
4
  • 02.01Mining real traffic
  • 02.02Synthetic augmentation
  • 02.03Labeling with humans
  • 02.04Versioning datasets
Module 3
Grading techniques
4
  • 03.01Deterministic checks
  • 03.02LLM-as-judge
  • 03.03Pairwise preference
  • 03.04Calibrating against humans
Module 4
CI and dashboards
4
  • 04.01Evals in pull requests
  • 04.02Trend dashboards
  • 04.03Alerting on regressions
  • 04.04Cost of evals
Module 5
Advanced topics
4
  • 05.01Multi-turn evaluation
  • 05.02RAG-specific metrics
  • 05.03Agent trajectory eval
  • 05.04Safety evals
Module 6
Capstone
4
  • 06.01Design a full eval suite
  • 06.02Wire it into CI
  • 06.03Instrument production
  • 06.04Present results
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