Prompt Chaining
"A surgical workflow technique where the output of one AI model call is used as the input for the next to solve complex, multi-step tasks."
Operator's Perspective
Trying to make an LLM solve a complex, multi-stage task in a single prompt is a recipe for hallucinations. Prompt chaining decomposes a complex task into discrete, manageable steps. The first prompt generates an outline, the second fills in details, and the third performs quality checks. This modular architecture makes debugging and updates significantly easier.
Technical Application
Use orchestration tools like n8n or LangChain to design pipeline paths. Each node in the pipeline takes the output of the preceding node, parses it, and feeds it into the next LLM call with a specialized system prompt.
Founder_Note
"Chaining saved our automated coding pipeline. Instead of asking one LLM to write, test, and style a component, we split it into three nodes. The reliability went from 40% to 98%."