What is prompt chaining?

Prompt chaining breaks a complex task into a series of smaller LLM prompts, where each step's output feeds into the next step's input, rather than solving everything in one call.

How is prompt chaining different from chain-of-thought prompting?

Chain-of-thought prompting asks a model to reason step by step within a single call. Prompt chaining explicitly splits the work across multiple separate calls, with programmatic logic in between.

Why use prompt chaining instead of one big prompt?

Smaller, focused prompts are generally more reliable, easier to debug, and allow developers to validate or correct intermediate results before proceeding to the next step.

What are the downsides of prompt chaining?

Each additional step adds latency and API cost, and managing state, retries, and errors across a multi-step chain adds engineering complexity.

Which frameworks support prompt chaining?

Frameworks like LangChain and LlamaIndex provide built-in abstractions for composing multi-step prompt chains, along with tool integration and memory.

Can prompt chains include tool calls?

Yes, a chain can interleave LLM calls with external actions like web searches, database queries, or API calls, feeding the results back into later prompts.

Is prompt chaining the same as an AI agent?

Not exactly. Prompt chaining is a fixed or lightly branching sequence of steps, while an agent typically makes autonomous decisions about which steps or tools to use next.

AI Fundamentals

Prompt Chaining

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Prompt chaining is a technique for solving complex tasks by breaking them into a sequence of smaller LLM calls, where the output of one prompt becomes the input to the next.

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Definition

Prompt chaining is a technique for solving complex tasks by breaking them into a sequence of smaller LLM calls, where the output of one prompt becomes the input to the next.

Overview

Rather than asking a language model to complete a complicated, multi-step task in a single prompt, prompt chaining decomposes the task into discrete stages, each handled by its own prompt. For example, summarizing a long document, extracting key entities from the summary, and then drafting an email based on those entities could be three separate prompts chained together, with each step's output feeding the next step's input. This approach improves reliability because each sub-task is simpler and more constrained, making it easier for the model to follow instructions correctly and easier for developers to validate intermediate outputs. It also allows different steps to use different models, temperatures, or tools — a cheap, fast model might handle simple extraction while a more capable model handles the final synthesis. Prompt chains can include conditional branching, where the next prompt in the chain depends on the content of a prior output, and can incorporate external tool calls or retrieval steps between LLM calls. Prompt chaining is a foundational pattern behind more sophisticated agentic workflows and orchestration frameworks such as LangChain and LlamaIndex, which provide abstractions for composing these multi-step pipelines. It differs from chain-of-thought prompting, which elicits step-by-step reasoning within a single model call, whereas prompt chaining explicitly separates reasoning into multiple distinct calls with programmatic control in between. The trade-off is increased latency and cost from multiple API calls, along with added engineering complexity to manage state, error handling, and retries across the chain.

Key Concepts

Decomposes a complex task into a sequence of simpler, focused LLM calls
Passes the output of each step as input to the next
Allows different models or parameters to be used at each stage
Supports conditional branching based on intermediate outputs
Enables validation and error handling between steps
Can interleave tool calls or retrieval steps with LLM calls
Improves reliability compared to a single monolithic prompt
Forms the basis of many agent orchestration frameworks

Use Cases

Multi-stage document processing: summarize, extract entities, then generate a report

Research assistants that plan, search, then synthesize findings

Content pipelines that draft, critique, and revise text in separate steps

Data extraction workflows that clean, structure, then validate output

Customer support bots that classify intent before generating a response

Code generation pipelines that plan, write, then test code in stages

Frequently Asked Questions

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