Скачать или смотреть Galileo Demo - Mastering AI Prompt Creation and Validation for Better Results

If your prompts pass the playground but break in production, this session is your missing layer: prompt validation with custom metrics, synthetic data, and a tight experiment loop. We show how to operationalize quality—so every change ships with evidence.

In this session, Paul teaches that reliable prompt development is an empirical process: define success with a custom, task-specific metric (here, mode/intent detection) , score outputs with LLM judges—including a three-judge panel and chain-polling for consensus , generate a synthetic dataset to probe edge cases and adversarial inputs , and run versioned experiments (V1 vs. V2) to compare metrics and iterate toward better prompts.

0:00 Why “vibe coding” fails
1:12 Define the goal → write a measurable metric
4:05 Build a synthetic dataset (coverage + negatives)
7:10 Set up LLM judges & scoring
10:32 Run the experiment loop (A/B prompts)
13:48 Analyze outcomes & iterate

👀 Watch to see how you can start building smarter custom metrics today.

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