Focusing on clearly stating needs before proposing implementations
Core Idea: Precisely articulating requirements before jumping to solutions enables more accurate and effective problem-solving, especially when working with LLMs that fill in ambiguities with probabilistic patterns from their training.
Key Elements
- Without clearly stated requirements, discussions often prematurely focus on specific solutions
- Well-defined requirements can sometimes uniquely determine the optimal solution
- LLMs know nothing about your specific requirements unless explicitly told
- Models fill requirement gaps with the most probable assumptions from their training data
- Underspecified requests lead to misaligned solutions that may be difficult to correct
Implementation Strategies
- Explicitly state all constraints and requirements before requesting solutions
- When misunderstandings occur, edit the original prompt rather than attempting corrections
- Specify known aspects of the solution that must work a particular way
- Be accurate about requirements; LLMs will try to follow inappropriate instructions
- Provide examples of similar solutions that meet your requirements
Impact of Requirements Clarity
- Small requirement changes can dramatically alter appropriate solutions
- Requirements help models navigate to the correct part of their latent space
- Clear requirements reduce the need for iterative corrections
- Explicit constraints prevent wasted effort on inappropriate solutions
Connections
- Related Concepts: Respect the Spec (honoring system boundaries), Culture Eats Strategy (environment influencing outcomes)
- Broader Context: Software Requirements Engineering (systematic approach to defining needs)
- Applications: Effective LLM Prompting (crafting precise instructions)
References
- Edward Z. Yang (2025). "AI Blindspots" collection, March 2025.
#requirements-engineering #software-development #llm-prompting #solution-design
Connections:
Sources:
- From: AI Blindspots