AI Models & Assistants / Knowledge library
Prompt Chaining Workflow
Passes the output of one bounded model step into the next predefined step
Why learn Prompt Chaining Workflow?
It improves control and debuggability for tasks that naturally follow a stable ordered process.
The problem it solves
It reduces the cognitive load and error surface of asking one model call to perform many distinct transformations.
Where it came from
Prompt chains decompose a task into a fixed sequence and can add programmatic gates between stages. They are more predictable than open-ended agents but less flexible when the necessary path depends on intermediate discoveries.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Define Prompt Chaining Workflow and identify its place in an agent architecture.
- Choose a task where Prompt Chaining Workflow improves an agent system.
- Diagnose one failure caused by misusing Prompt Chaining Workflow.
- Interpret a trace or state transition involving Prompt Chaining Workflow.
- Explain one operational tradeoff of Prompt Chaining Workflow.
Documentation and sources
- Building Effective AI Agents (www.anthropic.com)