Breaking work into stages
Prompt chaining decomposes a complex task into a sequence of smaller prompts, where the output of one becomes the input to the next. Instead of asking the model to do everything in a single call, each stage handles one focused subtask.
A typical chain
- Stage one extracts or cleans the raw input.
- Stage two transforms it, such as summarizing or classifying.
- Stage three formats the result for delivery.
Why chaining helps
- Each prompt is simpler, so the model performs each step more reliably.
- Stages are testable and debuggable in isolation.
- You can swap or reuse individual steps without rewriting the whole flow.
Tradeoffs
Chaining adds latency since steps run in sequence, and errors can propagate when a bad output feeds the next stage. Validate intermediate outputs so a failure is caught early rather than amplified. Keep the chain as short as the task allows.
Key idea
Prompt chaining splits a task into a pipeline of focused prompts that pass output forward, gaining reliability and testability at the cost of latency and error propagation.