Prompting 101: Show, don’t tell (www.haskellforall.com)

A recent exploration in prompt engineering highlights a common error when interfacing with large language models (LLMs): starting prompts with explicit instructions rather than illustrative examples. For instance, instructing an LLM by stating, "You are a Staff+ Software Architect with 40 years of experience," does not effectively leverage the model's pattern-recognition capabilities and can lead to less accurate responses. Instead, the article emphasizes the importance of showing the model how to behave through examples, akin to teaching a person through demonstration rather than direct instruction. This approach caters to the LLM's training paradigm, as it relies heavily on recognizing and mimicking patterns from its training data. The significance of this method is twofold. Firstly, it aligns with how LLMs are designed to understand and generate text, fostering more coherent and contextually appropriate responses. By illustrating desired conversational styles through mock interactions, users can improve the efficacy of their prompts. This technique not only enhances the conversational dynamics between the user and the model but also mitigates issues like hallucinations or inaccurate outputs, fostering a richer and more engaging user experience. Thus, embracing a "show, don’t tell" strategy represents a pivotal step toward optimizing LLM performance in practical applications.