Prompt Chaining (2026)
Prompt chaining splits a complex task into a sequence of focused LLM calls where each call receives the output of the previous one as input. It dramatically outperforms single-prompt approaches for tasks that require distinct stages — research, analysis, writing, review — because each focused prompt is easier to get right than one mega-prompt trying to do everything.
When to Use
- ✓Tasks with distinct stages that require different reasoning modes (research → outline → write → edit)
- ✓When a single prompt regularly hits the model's output length limit and needs to be split
- ✓When different stages of a workflow benefit from different models (cheap fast model for filtering, expensive model for generation)
- ✓When you need to validate or transform intermediate outputs before passing to the next stage
- ✓Content pipelines where the same transformation is applied to many items in sequence
How It Works
- 1Design each stage as a focused prompt with a single responsibility. Give it the minimum context it needs — not the full conversation history.
- 2Use structured output (JSON) at each stage to make handoffs programmatically reliable. A stage that returns free text is harder to pass to the next stage without manual parsing.
- 3Add validation gates between stages: if stage 1 produces output that fails validation, retry that stage before proceeding. Don't propagate errors through the chain.
- 4Use a different model for different stages based on the task difficulty: use a fast, cheap model for simple filtering/classification stages and a powerful model only for the generation stages.
- 5For long chains (5+ steps), consider using an orchestration framework like LangGraph, Prefect, or Temporal to manage retries, state persistence, and parallel execution.
Examples
Common Mistakes
- ✗Passing the full conversation history between every stage: Each stage should receive only the context it needs. Passing everything inflates token count and dilutes focus — the model gets confused about what it's supposed to do.
- ✗No validation between stages: If stage 1 outputs malformed JSON and you pass it directly to stage 2, you'll get garbage. Always validate and handle failures before proceeding.
- ✗Building chains that are too long (10+ stages): Very long chains amplify errors — each stage's mistakes compound. If you need 10+ stages, reconsider whether agent-based approaches with self-correction are more appropriate.
- ✗Not logging intermediate outputs: In production, chains can fail at any stage. Log every intermediate output so you can diagnose which stage caused the failure.
FAQ
When should I use prompt chaining vs a single complex prompt?+
Use chaining when: (1) the task has distinct phases, (2) intermediate outputs need validation, (3) you want to use different models for different stages, or (4) a single prompt regularly fails on the full task. A single prompt is fine when the task is unified and the model handles it reliably.
How is prompt chaining different from an agent?+
Prompt chaining is deterministic — you design the exact sequence of calls in advance. Agents are dynamic — the LLM decides which steps to take based on intermediate outputs. Chaining is more predictable and easier to debug; agents are more flexible for open-ended tasks.
Can different stages use different models?+
Yes, and this is a major advantage of chaining. Use GPT-4o-mini or Claude Haiku for simple filtering/classification stages (fast, cheap), and GPT-4o or Claude Sonnet for complex generation stages. This can cut total cost by 50–70% vs using a premium model for every stage.
How do I handle a failed stage?+
Implement per-stage retry logic (max 2–3 retries). If retries are exhausted, fail fast and return a structured error. Never silently pass a failed stage's output to the next stage — this makes debugging extremely difficult.
What frameworks help with prompt chaining?+
LangChain's LCEL, LangGraph, and Haystack all support explicit prompt chain definition. For production workflows needing durability (retries, state persistence), Temporal or Prefect are excellent. For simple cases, writing your own sequential function calls is perfectly adequate.