Prompt Chaining

Prompt chaining is a technique for orchestrating large language models (LLMs) by decomposing a complex task into a series of smaller, dependent subtasks, where the output of each prompt becomes the input for the next. Rather than attempting to solve a difficult problem in a single inference call, prompt chaining breaks the work into manageable stages — such as planning, drafting, critiquing, and refining — allowing the model to build progressively toward a more accurate or sophisticated result. This mirrors how humans tackle complex problems by working through intermediate steps rather than jumping directly to a final answer.

In practice, a prompt chain might begin by asking a model to extract key facts from a document, pass those facts to a second prompt that generates a structured outline, and then feed that outline to a third prompt that writes a polished summary. Each link in the chain can also include conditional logic, validation steps, or branching paths depending on the model's output, making the overall pipeline highly flexible. Frameworks like LangChain and LlamaIndex have formalized this pattern, providing developers with tools to construct, manage, and debug multi-step prompt pipelines at scale.

Prompt chaining matters because it dramatically expands what LLMs can reliably accomplish. Single prompts often fail on tasks requiring sustained reasoning, precise formatting, or domain-specific multi-step procedures, because the model must juggle too many constraints simultaneously. By isolating concerns across multiple prompts, each step becomes simpler and more controllable, reducing error accumulation and making outputs easier to inspect and correct. This also enables human-in-the-loop workflows, where a person can review or redirect the chain at critical decision points.

The technique became widely adopted alongside the rise of GPT-3 and GPT-4, as practitioners discovered that thoughtful prompt sequencing could unlock capabilities that brute-force single-prompt engineering could not. Prompt chaining is now a foundational pattern in agentic AI systems, retrieval-augmented generation pipelines, and automated reasoning workflows, sitting at the intersection of prompt engineering and software architecture.