Chaining Prompts for Agentic Reasoning

Full transcript

Imagine it's Saturday morning at a food bank, and a giant unsorted pile of donations has just arrived. Your job is to turn that chaos into something useful — and fast. Station one is the Sorter, whose only job is to place every item into the correct bin — canned goods, dry goods, fresh produce, or dairy. One role, one clear output. Station two is the Inspector, who checks every bin for expired or damaged items. Critically, the Inspector completely trusts that sorting was already done correctly by station one. Between stations, a quality control checkpoint asks one simple question: is this output good enough to pass forward? A bad input from the Sorter will break the Inspector's work entirely. Station three is the Packer, who builds family food boxes using only the approved items. The Packer never needs to understand sorting — they just need clean, reliable input from the previous step. This is prompt chaining. You break a complex task into discrete steps, where each step's output becomes the next step's input — just like the lead-to-cash cycle in a real business workflow. Every handoff in the chain must pass through a validation gate that confirms the output is correctly shaped before the next agent receives it. Good data and clear boundaries are what make the whole chain work.