Prompt chaining is the practice of breaking a complex task into a sequence of simpler subtasks, each handled by its own prompt, where the output of one step feeds into the input of the next. This divide-and-conquer approach enables workflows that would be unreliable or impossible to accomplish with a single monolithic prompt.
A typical chain might work as follows: the first prompt extracts key entities from a document, the second prompt researches each entity using retrieved context, the third prompt synthesizes the research into a structured analysis, and the fourth prompt formats the analysis into the desired output format. Each prompt in the chain is focused, testable, and optimized for its specific subtask.
Chaining offers several advantages over single-prompt approaches. Reliability improves because each step handles a simpler task with clearer success criteria. Debuggability improves because you can inspect intermediate outputs to identify where a chain fails. Flexibility improves because individual steps can be swapped, reordered, or parallelized without rewriting the entire workflow. Cost efficiency can improve because simpler steps can use smaller, cheaper models while only the complex reasoning step requires a more capable model.
Designing effective chains requires careful attention to the interfaces between steps. Each prompt must produce output in a format that the next prompt can consume. Structured output formats — JSON, XML, or clearly delimited sections — make these handoffs more reliable than free-form text. Error handling at each step should detect when output quality is insufficient and either retry or gracefully degrade.
Chain orchestration can be implemented in application code using simple sequential calls, or through dedicated frameworks that provide features like parallel execution of independent steps, conditional branching based on intermediate results, retry logic with backoff, and observability across the entire chain.
Managing chains in production requires versioning and testing at both the individual prompt level and the chain level. A change to one prompt in a chain can affect downstream outputs in unexpected ways. End-to-end test cases that validate the entire chain's output are essential, complementing the unit-level tests on each prompt.