{
  "id": "prompt-chaining",
  "code": "PTL-0065",
  "term": "Prompt Chaining",
  "aliases": [
    "LLM chains",
    "multi-step prompting"
  ],
  "category": "agents",
  "definition": "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.",
  "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.",
  "example": null,
  "broader": [
    "agentic-workflow"
  ],
  "narrower": [],
  "related": [
    "least-to-most-prompting",
    "routing"
  ],
  "introduced": 2021,
  "sources": [
    {
      "title": "AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts",
      "authors": "Wu et al.",
      "year": 2021,
      "url": "https://arxiv.org/abs/2110.01691"
    },
    {
      "title": "Building effective agents",
      "authors": "Anthropic",
      "year": 2024,
      "url": "https://www.anthropic.com/research/building-effective-agents"
    }
  ],
  "url": "https://protologue.com/t/prompt-chaining/",
  "citation": "Protologue. (2026). Prompt Chaining. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0065). https://protologue.com/t/prompt-chaining/"
}