Prompt chaining, sometimes referred to as Pipeline pattern, represents a powerful paradigm for handling intricate tasks when leveraging large language models (LLMs). Rather than expecting an LLM to solve a complex problem in a single, monolithic step, prompt chaining advocates for a divide-and-conquer strategy. The core idea is to break down the original, daunting problem into a sequence of smaller, more manageable sub-problems. Each sub-problem is addressed individually through a specifically designed prompt, and the output generated from one prompt is strategically fed as input into the subsequent prompt in the chain.