2 months ago
- Prompt Engineering is a systematic, experimental methodology for creating prompts to reliably extract information from language models, differing from 'Blind Prompting' which relies on crude trial-and-error.
- The process starts with defining a problem, building a demonstration set for testing, generating prompt candidates, and measuring accuracy with metrics like percentage correct and token usage.
- Prompt selection involves a cost vs. accuracy analysis, considering factors like model choice and token budget, followed by continuous verification and improvement using user feedback or parsing errors.
- The post emphasizes decomposing problems for tractability, keeping outputs simple, and normalizing results, while noting that prompting is a foundational skill for higher-order LLM integrations.