---
title: "Prompt Chaining"
description: "Prompt chaining is the process of using the output from one Large Language Model (LLM) call as input to another call in a sequence. It decomposes a task into smaller stages, allowing each prompt to focus on a narrower operation."
canonical_url: "https://promptmetheus.com/resources/llm-knowledge-base/prompt-chaining"
---

> Index of every page on this site: [llms.txt](https://promptmetheus.com/llms.txt)

Prompt chaining is the process of using the output from one [Large Language Model (LLM)](https://promptmetheus.com/resources/llm-knowledge-base/large-language-model-llm) call as input to another call in a sequence. It decomposes a task into smaller stages, allowing each [prompt](https://promptmetheus.com/resources/llm-knowledge-base/prompt) to focus on a narrower operation.

For example, one call may extract facts from a document, a second may organize them into an outline, and a third may draft a report. Different models can be used for different stages, and application code can validate intermediate results before continuing.

Prompt chaining is a common [agentic workflow](https://promptmetheus.com/resources/llm-knowledge-base/agentic-workflow) pattern, but a fixed chain is not necessarily an autonomous [AI agent](https://promptmetheus.com/resources/llm-knowledge-base/ai-agent). The sequence is usually predefined rather than selected dynamically by the model.

Long chains increase latency and create opportunities for errors to propagate. [Structured output](https://promptmetheus.com/resources/llm-knowledge-base/structured-output), explicit validation, retries, and [evaluation](https://promptmetheus.com/resources/llm-knowledge-base/evaluation) help make the boundaries between stages more reliable.

## Sitemap

See the full [sitemap](https://promptmetheus.com/sitemap.md) for all pages.