The video connects the course's pieces into one workflow, from datasets to production and back.

Foundation: evaluation datasets

  • Start by creating test cases that reflect your key scenarios, edge cases, and safety risks — these datasets become your foundation 0:47.

Experimental cycle

  • Iterate: test different models, prompts, and configurations 0:47.
  • Start with manual evaluations, then scale with automated metrics 0:47.
  • It is an ongoing cycle: make changes → run tests → refine, step by step 0:47.

Deployment and production

  • Once the app is well tested, deploy it; add guardrails to prevent bad outputs in production 1:05.
  • After deployment, "observability is all you need": collect real interaction data and run evals over it to see what's going on and catch issues 1:05.
  • When something goes wrong: return to the experimental cycle to test solutions, run regression tests, then deploy the fixes 1:05.

Closing the loop

  • Feed new failure cases back into your evaluation datasets — the data at the heart of evaluations becomes your major asset 1:35.
flowchart TD
    DS[Evaluation datasets] --> EXP[Experimental cycle:<br/>models, prompts, configs]
    EXP -->|well tested| DEP[Deploy + guardrails]
    DEP --> OBS[Observability:<br/>real data + evals]
    OBS -->|issue| EXP
    EXP -->|regression tests pass| DEP
    OBS -->|new failure cases| DS