Definition
Prompt Chaining is a prompting pattern where several model calls are linked so each step feeds the next one. In practical AI work, it helps teams connect a concept to data, model behavior, product choices, evaluation, and risk. The useful question is not only what the term means, but how it affects quality, cost, reliability, and decisions in a real workflow.
Example
A user improves an assistant workflow with Prompt Chaining so the model follows the task more reliably.
Why it matters
Prompt Chaining matters because a prompting pattern where several model calls are linked so each step feeds the next one can change how teams build, evaluate, choose, or govern AI systems. It directly affects how users ask for results, control outputs, evaluate quality, and avoid unsafe or misleading behavior.
How it works
A user or product flow provides instructions, context, examples, constraints, and sometimes intermediate steps, then the model generates or routes the next output. For Prompt Chaining, the key is to connect the definition with inputs, assumptions, measurable outcomes, and deployment limits.
Where it is used
- Used in chatbots, assistants, workflow automation, content tools, customer support, research, and internal knowledge systems.
Limitations
Prompt-based workflows can be brittle, sensitive to wording, and vulnerable to hidden instructions or missing context.
FAQ
Why is Prompt Chaining useful to know?
Prompt Chaining is useful to know because it affects practical decisions about model quality, cost, reliability, safety, or tool selection.
How should Prompt Chaining be evaluated in practice?
Start with the concrete task, then check the data, assumptions, metrics, limitations, and the cost of errors before relying on the result.