Prompt Chaining: Break Complex Tasks into Steps

Prompt chaining is the technique of breaking a complex task into a sequence of simpler prompts, where the output of one step becomes the input to the next. Instead of asking a model to do everything at once, you build a pipeline of focused steps.

Why Chain Instead of One Big Prompt?

A prompt asking a model to “analyze this document, extract themes, compare to strategy, and draft a summary” juggles four tasks at once — and often fumbles at least one. Chaining decomposes that into steps:

1. Extract key themes from the document
2. Compare extracted themes against strategy goals
3. Draft an executive summary from the comparison

Each prompt does one thing well. You can inspect the output at every stage, catch errors early, and adjust individual steps without rewriting the whole workflow.

Pattern: Self-Correction

One of the most practical chaining patterns uses a second prompt to review and improve the first prompt’s output:

Step 1 — Generate:
"Write a product description for {{PRODUCT}} targeting {{AUDIENCE}}."

Step 2 — Review:
"Review this product description against these criteria:
- Under 150 words
- Includes a clear call to action
- Avoids jargon
Flag any issues."

Step 3 — Refine:
"Revise the description to fix the flagged issues.
Return only the final version."

This generate-review-refine loop catches mistakes that a single prompt would miss. The review step acts as a built-in quality gate.

Pattern: Extract Then Synthesize

For document analysis, a two-step chain works well:

Step 1 — Extract:
"Read the document below and extract all quotes relevant
to the question: {{QUESTION}}"

Step 2 — Answer:
"Using only these extracted quotes, answer the question
in a clear, concise paragraph."

The extraction step forces the model to find evidence first, then the synthesis step composes a grounded answer. This dramatically reduces hallucination on long documents.

Tips

• Chain when the task has distinct phases or a single prompt produces unreliable results; use a single prompt when it’s straightforward
• Keep each step focused — one task per prompt produces the best results
• Pass only what’s needed — trim context to what each step requires
• Log intermediate outputs — debugging chains is far easier than debugging a monolithic prompt

Chains let you compose multi-step workflows from simple building blocks. But what happens when the input itself is massive — a 100-page contract or an entire codebase? Next up: working with long context.