Glossary

Prompt Chaining

Prompt chaining is an architectural technique where the output of one large language model prompt serves as the input for a subsequent prompt. By decomposing complex tasks into a sequence of smaller, modular steps, this method improves model accuracy, reduces hallucinations, and allows for the integration of external data or logic between processing stages.

In enterprise AI architecture, prompt chaining addresses the limitations of single-shot prompting, where models often struggle with multi-step reasoning or complex constraints. As organizations move toward autonomous agents, chaining provides a structured framework for managing state and context across long-running workflows. It allows architects to enforce modularity, making it easier to test individual components, swap models for specific sub-tasks, and implement guardrails at critical transition points, ultimately increasing the reliability of automated systems in production environments.

Practitioners implement prompt chaining by defining a directed acyclic graph of operations where each node represents a specific prompt or tool call. Developers must manage the context window carefully, as passing excessive data between links can lead to token exhaustion or performance degradation. Effective implementation requires robust error handling and validation logic at each link to ensure that the output of one step meets the schema requirements of the next, preventing cascading failures throughout the execution pipeline.

Last updated: 2026-08-25