What is prompt chaining? multi-step LLM guide

Prompt chaining is a design pattern that enhances the functionality of large language models (LLMs) by using the output of one model call as the input for the next, creating complex, multi-step workflows that single prompts cannot achieve. This approach improves quality by setting clear objectives for each step, facilitates easier debugging, allows the integration of different models, and reduces token costs. Common patterns include extract-then-act, classify-then-route, generate-then-validate, and summarize-then-synthesize. CodeWords operationalizes prompt chaining by enabling users to describe multi-step workflows that are then deployed as services with error handling, structured outputs, and external tool access, without the need for manual coding. It supports integration with various LLMs like OpenAI, Anthropic, and Gemini, and utilizes Redis for state persistence across multiple runs, offering over 500 integrations to connect chain steps to external data and services.

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