Definition: Prompt chaining is a technique that breaks a complex task into a sequence of smaller prompts, where the output of one step becomes input for the next.Why It Matters: One large prompt asking for everything often yields uneven results. Chaining makes each step simpler, easier to test and easier to control, and it allows checks between steps. The trade-offs are higher latency and cost, and errors that can carry from one step into the next.Key Characteristics: Each link has a focused instruction and expected output format. Steps may run different models, call tools or include validation. Chains can be linear or branch based on earlier results.
The application sends the first prompt, for example extracting facts from a document. It validates the output and passes it into the next prompt, such as drafting a summary from those facts. A further step might review the draft against a checklist. Between steps, code can check formats, filter content or stop the chain. Each step is logged with its input and output so failures can be located. Teams test each link separately and the chain as a whole.ProsChains improve quality and make behavior easier to test and debug.ConsMore calls mean more latency and cost, and an early error can propagate.
Report Generation: A chain extracts data, drafts sections, then checks tone and consistency.Data Extraction: A first step identifies entities, and a second normalizes them into a fixed format.Content Review: A draft is generated and then reviewed by a separate prompt against compliance rules.
Single Prompts (2020-2021): Early use relied on one prompt per task.Step-by-Step Reasoning (2022): Research showed that stepwise prompting improved reasoning.Chain Frameworks (2022-2023): Libraries made it simple to link prompts and tools.Current Practice and Trends (2023-Present): Chains are embedded in agentic workflows with validation and monitoring.
When to Use: Use chaining when tasks have clear stages or need checks between steps.Designing for Reliability: Validate outputs between steps, and test each link alone.Operating at Scale: Cache repeated steps, run independent steps in parallel and track cost per chain.Governance and Risk: Log inputs and outputs per step, and apply data controls at each link.
