LLMs work best when the user defines their acceptance criteria first
The core argument presented is that Large Language Models (LLMs) achieve optimal performance when users clearly define their acceptance criteria upfront. This principle is particularly crucial in applications like code generation, where the correctness and functionality of the output are paramount. Without explicit and detailed criteria, LLMs often produce results that, while syntactically plausible, fail to meet the specific requirements or intended behavior. The article implicitly suggests that a structured approach, similar to Test-Driven Development (TDD) in software engineering, can significantly enhance the efficacy of LLMs. By providing concrete examples or test cases alongside the initial prompt, users can guide the LLM towards generating more accurate and verifiable outputs, thereby mitigating issues related to incorrect or incomplete responses. This highlights the importance of precise prompt engineering and structured input for maximizing LLM utility.