Better Prompting LLMs Through Analogies

A new prompting strategy for large language models uses analogies to reduce ambiguity and align tasks with the model's learned patterns, resulting in faster, more accurate outputs. By framing instructions as familiar comparisons, the approach minimizes hidden reasoning and cuts down on token usage and retries. This method lowers inference costs while improving response reliability.

How to better prompt LLMs Make the next token easier to choose A good prompt reduces ambiguity reduces conversion work and makes the desired operation match the model's learned patterns. Accurate Clear inputs and success criteria produce fewer wrong branches. Fast Less hidden reasoning means the model reaches the answer sooner. Fewer Tokens Compact structure avoids repeated clarification and repair. Lower Cost Shorter inference and fewer retries cut usage spend.