Prompt Chaining
Prompt chaining breaks complex tasks into sequential LLM calls where each step's output feeds into the next.
Instead of relying on a single prompt to handle everything, you design a pipeline of focused calls — each performing one step — that combine to produce reliable, structured results. A foundational pattern for AI workflows that need precision, auditability, or multi-stage reasoning. Also known as: Chained Prompts, Sequential Prompting
What this topic covers
- Foundations — Prompt chaining turns a single complex task into a pipeline of focused, sequential LLM calls.
- Implementation — The guides cover how to structure prompt chains, pass state between steps, and handle errors — using the frameworks and patterns most teams rely on in production today.
- What's changing — Prompt chaining is being reshaped by longer context windows, smarter orchestration libraries, and a shift toward autonomous agents.
- Risks & limits — When a chain of LLM decisions produces an outcome, accountability for errors becomes unclear.
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