Navigating the Uncharted: Ensuring Prompt Quality in the Age of Language Models
summary
A production prompt that summarized calls begins hallucinating on short calls and surfaces false actions, so an iterative process uses manual QA (content experts), LM-based judging (e.g., GPT‑4 rating GPT‑3 outputs) showing about 80% correlation with humans, off‑the‑shelf NLP checks (sentiment, NER, toxicity), user signals (thumbs, dwell time) and simple automatic metrics like output/input length ratio. A 200‑example sampling finds edge cases quickly; tradeoffs are cost, speed and model drift when switching LMs.
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chapters (9)
- 0:00Introduction to prompt quality
- 3:36What is a prompt
- 4:37When to evaluate prompts
- 7:10Manual QA for prompts
- 8:14LM judge for evaluation
- 10:25Automated NLP checks
- 12:28User feedback in the loop
- 13:53Automated metrics for prompts
- 15:18Summary and Q&A
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