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Dictionary

Prompt Chaining

Pipelining LLM calls where each step's output feeds the next.

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Prompt Chaining is the practice of decomposing a complex task into a sequence of smaller LLM calls, each handling one transformation, to increase reliability and debuggability. Pipelining LLM calls where each step's output feeds the next.

Pipelining LLM calls where each step's output feeds the next. Prompt Chaining is the practice of decomposing a complex task into a sequence of smaller LLM calls, each handling one transformation, to increase reliability and debuggability. In practice: Step 1 extracts entities, step 2 classifies them, step 3 writes a summary referencing each classification. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
Prompt Chaining is the practice of decomposing a complex task into a sequence of smaller LLM calls, each handling one transformation, to increase reliability and debuggability.
Example
Step 1 extracts entities, step 2 classifies them, step 3 writes a summary referencing each classification.
Related Workflows
Related Prompts
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What is Prompt Chaining?

Prompt Chaining is the practice of decomposing a complex task into a sequence of smaller LLM calls, each handling one transformation, to increase reliability and debuggability.

What is an example of Prompt Chaining?

Step 1 extracts entities, step 2 classifies them, step 3 writes a summary referencing each classification.

Why does Prompt Chaining matter for AI and automation?

Pipelining LLM calls where each step's output feeds the next. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.

/ topics#ai#prompting
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