Prompt Chaining Strategies: Splitting Complex Tasks Across Stages
Complex tasks split across chained prompts outperform single prompts. Here is how I design chains that stay reliable. Prompt chaining is the practice of splitting a complex task across multiple prompts, where the output of one becomes the input to the next. A single prompt asked to research, outline, draft, and edit a report produces a worse result than four prompts that each handle one stage, because a single prompt has to split its attention and the later stages suffer. Chaining lets each stage focus, but it introduces its own failure modes: errors propagate, the handoff format has to be consistent, and debugging requires tracing across links. After building several chained pipelines, I have strategies that keep chains reliable. This guide covers them. When to Chain and When Not to Chaining costs more tokens and more latency than a single prompt, so I chain only when the task genuinely benefits from separation. A task benefits from chaining when its stages have different goals that conflict inside one prompt. Research wants breadth, drafting wants focus, and editing wants critique, and a prompt asked to do all three compromises each. When the stages are cooperative rather than conflicting, a single prompt is better, because the model keeps full context across the work. Chain when stages have conflicting goals or need different instructions. Chain when each stage needs a different model or settings.