What is prompt chaining? Multi-step LLM guide

Prompt chaining is a design pattern used in AI automation, where the output of one large language model (LLM) call serves as the input for the next, creating a sequential workflow that addresses tasks too complex for a single prompt. This technique improves quality by allowing each step to have a narrow, clear objective, simplifies debugging through step-by-step inspection, and reduces token costs. By employing different models for different tasks, prompt chaining ensures each step is appropriately handled. The process typically involves three elements: steps, data passing, and control flow, and can be applied in patterns such as extract-then-act, classify-then-route, generate-then-validate, and summarize-then-synthesize. CodeWords facilitates the operationalization of prompt chaining by enabling multi-step workflows with structured outputs and error handling, and offers access to various LLMs and external integrations without the need for managing multiple API keys.

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