The Solution: Build a Production Line

Prompt chaining is the technique of breaking a complex task into a sequence of smaller, single-purpose prompts, where the output of one step becomes the input of the next. Instead of asking the model to research, structure, write, and edit an article all in one giant instruction, you run four focused calls in a row. It's like an assembly line in a factory: each station does one job well, and the half-finished product moves forward through clearly defined stages until it's complete.

How it works

Each link in the chain is a normal prompt — what makes it a chain is that your code, not the model, decides the order and passes data between steps. To keep the handoffs reliable, ask each step to return structured output (for example JSON) so the next prompt can read specific fields instead of parsing free-form prose. When a step needs real data or an action, function calling lets the model trigger a tool and feed the result into the chain. Because each call is independent, you can even use a different model or temperature per stage — a creative high-temperature model for brainstorming, then a precise low-temperature model for fact-checking.

When to use it — and the tradeoffs

Reach for chaining when a single prompt produces vague or inconsistent results, when the task has genuinely distinct phases (extract, then analyze, then summarize), or when intermediate results need validation before continuing. The big wins are reliability and debuggability: each step is short, easy to test, and easy to fix in isolation. The costs are real, though — more calls mean higher latency and price, and errors can cascade, since a mistake or hallucination early on poisons every step that follows. As a concrete example, to turn a customer review into a support ticket you might chain: (1) extract entities and the complaint as JSON, (2) classify sentiment and urgency from that JSON, then (3) draft a reply that uses both. Each step is trivial to verify on its own, whereas one mega-prompt doing all three at once is far harder to trust or correct.

Where Is This Used?

• Content Pipelines: Research → Draft → Edit → Format
• Data Processing: Extract → Transform → Validate → Load
• Analysis Workflows: Classify → Summarize → Recommend
• Code Generation: Plan → Implement → Review → Test

Try It Yourself!

Use the interactive example below to see how chaining multiple prompts creates more sophisticated outputs than a single prompt could achieve.