Prompt Chaining

Prompt chaining is a design pattern in AI applications where a complex task is decomposed into a series of simpler sub-tasks, each handled by a separate prompt. The output of each step is processed and fed as input to the next step, creating a chain of operations that collectively accomplish the larger goal. For example, a document analysis workflow might chain three prompts: first, extract key data points from a document; second, classify the document based on the extracted data; third, generate a summary tailored to the classification. Each step is simpler and more reliable than trying to accomplish everything in a single prompt. Prompt chaining is a middle ground between single-prompt approaches (which struggle with complex tasks) and fully autonomous agent architectures (which can be unpredictable). It provides the structure of a predefined workflow while leveraging the flexibility of language models at each step.

Prompt chaining dramatically improves the reliability and quality of AI-powered workflows for complex tasks. By breaking tasks into discrete steps, each step can be optimised independently, validated before passing results downstream, and debugged more easily when issues arise. This pattern also enables important engineering practices. Each step can use a different model (cheaper models for simple steps, more capable models for complex ones), have its own error handling and retry logic, and be monitored independently. This modular approach makes AI workflows more maintainable and cost-effective. Common business applications include multi-step document processing, customer support triage-and-response pipelines, content generation workflows (research, outline, draft, edit), and data analysis pipelines that combine extraction, analysis, and reporting steps.