Prompt Chaining

Prompt chaining is an application design pattern in which multiple foundation model interactions cooperate to complete a task. An earlier interaction produces information that a later prompt consumes, such as extracted facts, a classification, a draft, or a review result. The application controls how these intermediate results move between steps. A chain can be linear, but a more sophisticated system can branch, combine independent results, or repeat a bounded refinement step.

Consider an application that answers questions about insurance policies. Pre-processing first normalizes the request and removes unnecessary sensitive fields. One model interaction identifies the policy type and required information. The application validates that output, retrieves documents the caller is authorized to access, and passes relevant excerpts to a second prompt that drafts an answer with source references. A final validation stage checks required fields and whether cited documents were actually supplied. Unsupported answers follow a clarification or human-review path rather than being returned automatically.

Each step should have a clear contract. Specify the input fields, expected output structure, permitted values, and failure behavior. Prefer structured intermediate data when downstream logic needs reliable parsing. Validate syntax and business constraints in application code; requesting JSON does not guarantee valid JSON or correct facts. Pass only the context needed by the next step rather than appending every previous message, which can increase token costs and expose irrelevant or sensitive information.

Conditional branching should use explicit, validated signals. For example, a classification result can route a billing question to one prompt and a technical question to another. Missing evidence can trigger retrieval or clarification. A model's self-reported confidence is not a calibrated reliability measure, so it should not be the sole basis for sensitive decisions. Define retry limits, timeouts, fallback responses, and termination conditions to prevent uncontrolled loops.

In cloud applications, orchestration can reside in application code, a managed model workflow, or a general-purpose workflow engine. Amazon Bedrock Flows, also described as prompt flows, connects prompt execution with conditional routing and supporting processing steps. AWS Step Functions can coordinate model calls, AWS Lambda functions, approval steps, and error handling. Strands Agents and AWS Agent Squad are code-based agent orchestration frameworks rather than managed workflow services; they can support applications containing chained model interactions, but agent coordination and prompt chaining are not synonymous.

Reusable prompt components should separate stable instructions, task-specific templates, examples, and runtime data. Version templates and workflow definitions together with model identifiers, inference settings, and validation rules. Evaluate individual steps and complete workflows against representative test cases, including malformed responses, adversarial inputs, missing evidence, and branch boundaries. Record execution identifiers, selected paths, latency, token usage, and validation outcomes, while applying appropriate controls to any logged prompt or response content.

The main trade-off is greater control at the cost of more model calls, additional failure points, and accumulated latency. An incorrect extraction can contaminate every downstream result, and a second model reviewing the first model's answer does not guarantee correctness. Use deterministic code for calculations, authorization, and enforceable policy checks. Treat retrieved text and intermediate model output as untrusted data, not instructions. Prompt chaining is also distinct from chain-of-thought prompting: it coordinates separate application interactions and does not require access to a model's private reasoning.