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."
Practical Usage
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.
How it Works
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.
Author Note
"Try separating generation and verification when the task benefits from distinct checks. Measure failures at each stage; adding another model call does not itself establish reliability."