Prompt Chaining Explained

Prompt chaining means breaking a complex task into smaller prompts where each output feeds the next step. Instead of asking for strategy, copy, critique, and final polish in one giant request, you create a sequence.

Chains are useful because models often perform better when each step has one clear job.

A reliable chain is analyze, draft, critique. Step one analyzes the source material and identifies the key issues. Step two creates the draft using that analysis. Step three critiques the draft against the goal and constraints.

This structure works for articles, emails, code reviews, product briefs, lesson plans, and many other workflows.

Use a chain when the task has multiple modes of thinking. Research and writing are different. Brainstorming and editing are different. Strategy and production are different. A single prompt can blur those modes together.

Chains also make it easier to inspect quality. If the final answer is weak, you can see whether the analysis, draft, or critique step caused the problem.

Each step should say what it receives and what it must return. For example: Use the analysis from Step 1. Do not introduce new claims. Return a table with recommendation, evidence, risk, and next action.

Explicit handoffs reduce drift and make chains reusable.

Step 1: Analyze [source] for [goal], return findings and assumptions. Step 2: Use those findings to draft [deliverable] for [audience]. Step 3: Critique the draft against [criteria]. Step 4: Produce the final version with changes applied.

That template is simple, but it is enough to make many AI workflows more reliable.