# Prompt Chaining

> Prompt chaining decomposes a task into a fixed sequence of model calls, where each call processes the output of the previous one, often with programmatic checks between steps.

- Identifier: PTL-0065
- Category: Agents & Orchestration
- Canonical URL: https://protologue.com/t/prompt-chaining/
- Also known as: LLM chains, multi-step prompting
- Introduced: 2021

## Description

Chaining trades latency for accuracy by making each call simpler. Wu et al. found chaining also improved transparency and controllability for users building with models.

## Broader terms

- [Agentic Workflow](https://protologue.com/t/agentic-workflow/)

## Related terms

- [Least-to-Most Prompting](https://protologue.com/t/least-to-most-prompting/)
- [Routing](https://protologue.com/t/routing/)

## Sources

- Wu et al. (2021). AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts. https://arxiv.org/abs/2110.01691
- Anthropic (2024). Building effective agents. https://www.anthropic.com/research/building-effective-agents

## Cite this entry

Protologue. (2026). Prompt Chaining. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0065). https://protologue.com/t/prompt-chaining/

License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)