AI Prompt to Chain Multiple Prompts Into a Multi-Step Workflow
This prompt chaining template helps you break a complex task into a sequence of smaller prompts, where the output of one step becomes the input to the next. It's for anyone building a multi-step AI workflow — research-then-write pipelines, extract-then-summarize tasks, draft-then-critique loops — who keeps getting weaker results from a single giant prompt than from a series of focused ones.
A single long prompt asking a model to "research this topic, then outline it, then write it, then edit it" tends to blur each stage together, so the model rushes the research to get to the writing. Prompt chaining fixes this by defining each step as its own self-contained prompt with a clear input and a clear output, so you (or an orchestration script) can inspect, correct, or regenerate any one step without redoing the whole chain. This works the same way whether you're running it manually turn-by-turn in ChatGPT or Claude, or wiring it into an automated pipeline.
If you're designing a chain with several steps, it helps to see how token and latency costs compare between a single combined prompt and the chained version before committing to either approach — Compare & Diff lets you put both versions side by side and see the tradeoff directly.
Prompt template
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Example input
Example output
Challenge
- The team relied on a spreadsheet-based process to track renewals and missed a critical deadline as a result.
- Manual tracking made it hard to see which accounts needed attention before it was too late.
Solution
- The team adopted the product and completed onboarding in under a week.
- Automated reminders replaced the manual tracking that had caused the missed renewal.
Results
- The team now saves roughly 6 hours a week that was previously spent on manual reporting.
- Automated reminders became the team's favorite feature for keeping renewals on track.
When to use it
- When a single prompt produces shallow or rushed output because it's asking for too many things at once
- When you need to inspect or correct an intermediate result (like a draft outline) before the model continues
- When building an automated pipeline where each step's output feeds the next step's input
- When different stages of a task genuinely need different instructions, tone, or constraints
Best practices
- Define a clear, minimal output format for each step so the next step can parse it reliably
- Keep each prompt focused on exactly one job; resist folding two steps back into one for convenience
- Pass only what the next step actually needs forward, not the full conversation history, to avoid drifting context
- Review each intermediate output once while designing the chain, since errors at an early step compound through every step after it
Common mistakes
- Chaining steps that don't actually need to be separate, which adds latency and cost for no real quality gain
- Not specifying the exact output format of a step, so the next step receives inconsistent input it can't parse
- Letting errors from an early step silently pass through instead of validating output before moving to the next step
- Carrying the entire prior conversation into every step instead of only the specific result each step requires
FAQs
What is prompt chaining?
Prompt chaining is the practice of breaking a complex task into a series of smaller, focused prompts, where each prompt's output becomes the next prompt's input, instead of trying to get a single prompt to handle every stage of the task at once.
When should I use prompt chaining instead of one big prompt?
Use it when a single prompt is producing shallow results because it's juggling too many instructions, when you need to check or correct an intermediate result before continuing, or when you're building an automated pipeline that needs each step's output in a specific, parseable format.
Does prompt chaining work the same way across ChatGPT, Claude, and Gemini?
Yes, the underlying technique is the same across models since it's about how you structure your own prompts and pass data between them, not a model-specific feature. The main difference is how much context each model can hold, which affects how much of the chain's history you can afford to carry forward.
Is prompt chaining more expensive than a single prompt?
It can use more total tokens than one combined prompt since each step repeats some context, but it often produces better results per step, so it's worth comparing the two approaches directly rather than assuming one is always cheaper.
What tool pairs well with testing a prompt chain before building it?
Compare & Diff — it lets you compare a single combined prompt against a chained version side by side, including estimated token and latency cost, so you can see whether splitting the task is actually worth it before you build the pipeline.