Prompt chaining pipelines

Some tasks are too complex for a single prompt. Writing an essay, analyzing a dataset, or building a report all need multiple steps. Prompt chaining breaks complex tasks into a pipeline where each prompt handles one step and passes its output to the next.

The basic chaining pattern: Step 1 (Extract): "Extract all person names from this text: ..." Step 2 (Process): "Alphabetize this list of names: [output from step 1]" Step 3 (Format): "Format these names as a numbered list: [output from step 2]" Each prompt is simple and focused. The chain handles complexity that a single prompt would struggle with. You can also add a self-correction step: "Check your work from the previous step and fix any errors."

Self-correction chains are especially powerful. After generating content, add a second prompt that reviews and improves it: Prompt 1: "Write a one-paragraph product description for wireless earbuds." Prompt 2: "Review this description. Fix any factual errors, improve clarity, and make it more compelling: [output from prompt 1]" The second prompt acts as an editor, catching mistakes the first prompt made.

A long prompt asks the model to juggle multiple tasks at once, which degrades quality. Chaining lets each step focus on one task with full attention. It also lets you inspect intermediate results, retry failed steps, and use different models or temperatures per step. Think of it as the "Unix philosophy" of prompting: do one thing well, then pipe the output.