Prompt chaining

Splitting a complex task into a sequence of smaller prompts, where each step's output becomes the next step's input.

Last updated: November 9, 2026 · Markdown version

How to apply it in prompts

Use this concept to reduce ambiguity and improve consistency. If you need strict formatting, combine it with a JSON guide and a per-model template.

Strict JSON Models Packs

What it means

Definition: instead of one giant prompt, you run several focused ones in order — for example extract the facts, then draft, then critique, then rewrite — passing results forward.
Why it matters: each step has one job, so it is easier to specify, test and fix. You can also check or correct the output between steps, which a single long prompt does not allow.
How to apply it: define the output format of every step so the next one can consume it, keep intermediate results short, and add a validation step where a mistake would be expensive.
Common mistake: chaining when one prompt would do. Every extra step adds latency and cost, so split only when the task is genuinely multi-stage or when one step keeps failing.

Copy-paste example

Step 1 of 3 — extract.
From the text below, list every factual claim as a numbered list. One claim per line, no commentary.

Text:
"""[paste]"""

(Next step: paste this list into step 2, "draft a summary using only these claims"; then step 3, "check the summary against the list and flag anything not supported".)

Opens the analyzer with the prompt prefilled.

Open in Promptea

FAQ

When is chaining worth it?

When the task has distinct stages, when one big prompt gives inconsistent results, or when you want to inspect or fix the output midway.

How is it different from chain-of-thought?

Chain-of-thought happens inside one response (the model shows its steps). Chaining uses several separate requests that you control.