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What is it?
Prompt chaining is a technique that splits a complex task into a series of consecutive prompts. Each prompt handles a defined part of the work and passes its result to the next step. The final output is the combined product of the entire chain.
Rather than asking a model to do everything at once, which increases the chance of errors and makes the outcome harder to verify, prompt chaining gives you control over every link. You can adjust, review, or replace one step without disrupting the rest of the chain.
Why it matters for SMEs
For SMEs, prompt chaining makes AI workflows more reliable and easier to maintain. A long, all-in-one prompt is fragile: a small change to the task description can upset the entire output. A chain of targeted steps is more robust and more transparent.
- Better quality control at each stage: you can review the output of every link before it becomes input for the next, so errors are caught early rather than at the end of a long process.
- Easier to adjust and debug: if one step does not work well, you replace or improve only that link, without rebuilding the rest of the workflow.
- Suited to longer and more complex tasks: summarising, validating, and then converting a document into a different format is straightforward to build and test step by step as a chain.
Teams that master prompt chaining can build more reliable AI processes without advanced programming knowledge. It is a foundation for building agents and automations that perform consistently.
How it works
Prompt chaining works by storing the result of each prompt and passing it as input to the next one. Every step has a clear purpose and an expected output shape. The chain as a whole describes the complete process.
- Task decomposition: split the overall task into logical stages, each with a clear input and output.
- Execute step 1: the first prompt processes the raw input and produces an intermediate result, such as a structured summary or a classified document.
- Pass forward: the output of step 1 is included as input in the prompt for step 2.
- Subsequent steps: each following prompt builds on the previous result until the final output is reached.
- Optional validation: between steps you can insert a check that assesses whether the output meets requirements before the chain continues.
In simple cases you run the steps manually. In automated workflows a script or agent handles the handoff from one prompt to the next.
Example in practice
Picture a construction company processing incoming quote requests for maintenance work. The first prompt reads the request and extracts the type of work, location, desired date, and contact details into a fixed structure. The second prompt compares that structure against the available capacity schedule and assesses whether the request fits within the next two weeks. The third prompt then drafts a confirmation or an alternative proposal based on that information. The planner reviews the final output and sends it with a single click.
Comparison and misconceptions
A single prompt asks a model to do everything at once; prompt chaining spreads the same task across several focused steps. Prompt chaining offers more control and makes debugging simpler, but requires more setup than a standalone prompt.
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