Sequential task decomposition where each model output feeds the next input.
Prompt chaining breaks a complex task into a sequence of smaller, focused prompts. Each step's output becomes the next step's input, creating a pipeline where quality can be checked and controlled at every stage.
Rather than asking a single large prompt to do everything at once, prompt chaining assigns each stage of a task to a dedicated model call with a specific, narrow objective. The output of step one is validated and formatted before it becomes input to step two. This continues until the final output is produced. The chain can be linear, branching, or conditional — where the path taken depends on what earlier steps produced. The critical design decision is where to place validation gates: checkpoints that inspect intermediate outputs before passing them forward.
A financial services firm uses prompt chaining for earnings report analysis. Step 1 extracts raw financial figures from the PDF. Step 2 normalises the figures against prior quarters. Step 3 identifies notable variances. Step 4 drafts analyst commentary. Step 5 formats the output for the client portal. Each step is logged and the output is validated against a schema before advancing. If step 3 produces no variances above threshold, the chain terminates early.
Monolithic prompts that attempt to do everything at once are extremely difficult to debug, audit, or improve. Prompt chaining makes each stage independently testable and replaceable. When something goes wrong, you know exactly which stage failed and why.
How this pattern fails in practice — and what to watch for.
A hallucination or formatting error at step 1 corrupts every downstream step. By step 6, the output is confidently wrong, with no indication that the error originated at step 1. Without inter-step validation, the chain amplifies initial errors.
Information from an earlier step leaks into a later step in unintended ways — for example, a customer name from one section of a document appearing in a summary of a different section. Particularly dangerous in multi-document pipelines.
When a step's output exceeds the next step's context limit, the overflow is silently dropped. The downstream step processes an incomplete input but has no way of knowing this, producing confident but fundamentally incomplete outputs.
Seven things to verify before deploying this pattern in production.
Prompt chaining appears in the AIDA exam as a core deployment pattern under D2 Output Validation — specifically around inter-step validation design and failure propagation. CAIG candidates need to understand the governance implications: which steps require a human checkpoint and what the audit trail should look like. CAIAUD auditors are expected to be able to inspect a chain's logging architecture and identify where validation gates are absent.
The AIDA certification covers all 21 agentic design patterns with a focus on deployment safety, governance, and the PSF. Free to attempt.