prompt-engineering

To craft LLM prompts with clear goals and constraints, define the WHAT and WHY instead of the HOW. This ensures prompts are structured with explicit success criteria and context to guide behavior consistently.

LLM prompts produce misaligned outputs when they lack explicit goals and constraints. Defining the WHAT and WHY rather than the HOW ensures prompts have the required context and structure to guide behavior consistently.

The best way to update existing prompts is using an interview-driven discovery process to surface domain knowledge and edge cases. This structured prompt maintenance encodes explicit statements and avoids anti-patterns for consistent AI behavior.

Yes, you can use an interview-driven discovery process to create prompt specifications across domains and languages. It surfaces required context, user types, and constraints with recommended-option marking to reduce cognitive load.

You should not use an automated prompt specification approach when your task requires encoding the HOW rather than the WHAT and WHY. This approach strictly enforces defining goals, constraints, and structure to avoid anti-patterns.