Prompt Chaining

Prompt chaining is the technique of breaking a complex AI task into a sequence of smaller prompts, where the output of each step feeds as input to the next. Rather than asking one prompt to do everything, you decompose the work into focused stages — research, analyse, summarise, format — each handled by a separate, specialised prompt. This produces more reliable results and makes failures easier to diagnose and fix.

Use prompt chaining when: a single prompt produces inconsistent or poor-quality results; the task has distinct sequential stages (e.g. research → write → edit); you need to validate or transform outputs between stages; or the task is too complex to fit cleanly in one prompt. Simple tasks do not need chaining — it adds overhead. Reserve it for workflows where quality and reliability matter and single-prompt approaches fall short.

A single complex prompt asks the AI to perform multiple tasks simultaneously, which can cause the model to shortcut steps or blend them together. Prompt chaining forces sequential, focused completion of each stage independently. This makes each step easier for the model (one job at a time), allows you to review outputs at each stage, and lets you retry or adjust individual steps without rerunning the whole workflow. Chaining trades speed for reliability and quality.

In automated workflows (LangChain, LlamaIndex, or custom code), prompt chains pass outputs programmatically between prompts without manual copying. Each prompt is a node, and the chain defines how outputs flow between them. You can add conditional branching (different next prompts based on output content), validation steps (a separate prompt checks quality before proceeding), and loops. This is the architecture behind most serious LLM applications.

3-5 steps is typical for most content and data workflows. Each step should do one clearly defined thing. If you find yourself writing a step that does two things, split it. If adjacent steps feel redundant, merge them. The goal is the minimum number of steps that reliably produces your desired output. Very long chains (10+ steps) can compound errors — earlier mistakes propagate through all subsequent steps.