Dating with a Super Model: Why Good Prompt Engineering for Data Monitoring Requires Some Flirting
summary
Builds an automatic data-monitoring pipeline for tens of use cases and reports three concrete lessons: large generative models often hallucinate or bluntly ignore long, broad prompts so alerts misfire; splitting a long instruction into a sequence of short prompts and forcing take it slowly (step‑by‑step) fixes consistency — demonstrated by turning a bad poem into one that meets five constraints; giving positive reinforcement for desired outputs and using the API to play the roles (system, assistant, user) produces reliable alert signals and usable business playbooks.
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chapters (7)
- 0:00Introduction and setup
- 1:10Context on data monitoring
- 3:01Problems with GenAI prompts
- 4:09First lesson: Take it slowly
- 6:43Second lesson: Praise the model
- 8:13Third lesson: Play roles in API
- 11:10Recap and closing remarks
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