Prompt Chaining means breaking a complex task into several separate prompts executed across multiple calls, where the output of one call becomes the input to the next — link after link, like a chain, until the whole task is complete. This is easy to confuse with chain of thought given the similar name, but the two operate at entirely different levels. Chain of thought is reasoning unfolded within a single reply, where you see the complete thinking process in one answer. Prompt chaining is task decomposition spread across multiple independent calls, where each call is its own separate request and reply, often with room for human review or adjustment in between. Put simply, chain of thought handles whether this one answer's reasoning gets written out, and prompt chaining handles whether the task gets split into several rounds — the two can be used together, and are frequently mistaken for two names for the same thing.
This is needed because some tasks are genuinely unrealistic to specify all at once — not because the instruction was poorly written, but because how the second half of the task should proceed depends on what the first half's output actually looks like. For example, have Claude summarize the key points from a pile of meeting notes first; once summarized, you might find several are actually irrelevant and should be cut, and only then move to turning the summary into a weekly report. If 'summarize and turn into a weekly report' were dumped into a single call, there would be no chance to check the summary quality in between, and a flawed summary would carry straight through into the final report — something you might not discover until the very end, forcing a complete redo. Prompt Chaining exists to insert checkpoints at the task's key junctures, catching errors while they're still small instead of letting them accumulate until they surface at the end.
In practice this works at two levels. The first is task decomposition: identifying the points in the task that genuinely need to be staged — usually where this step's output quality directly determines how the next step should proceed, such as classify then process, summarize then rewrite, draft then fact-check. Not every task is worth splitting; if each step's output is easily predictable and rarely goes wrong, breaking it into multiple steps just adds unnecessary operational overhead. The second level is the handoff: what form the previous step's output takes when it's passed to the next step — pasted over verbatim, or first quickly filtered by a human to strip out what isn't needed. This handoff is worth deliberately designing, because a poorly designed handoff plants a new source of error right in the middle of the chain — if the previous step's output contains irrelevant content and it's fed straight into the next step unfiltered, the model in that next step may end up treating that irrelevant content as something worth acting on too.
For you, the real value of Prompt Chaining is that once a large task is broken apart, each step can be checked and re-run individually, and when something goes wrong, only the faulty step needs fixing — not the whole process starting over. This becomes more obvious as the task grows more complex and involves more judgment calls: dump one complex task on Claude in a single shot, and when it goes wrong it's often hard to tell which part broke; split into multiple steps, and which step's output is faulty becomes obvious at a glance. What's actually worth weighing is the operational cost of decomposition itself — each additional step means another call and another round of checking, and for simple tasks that cost can exceed whatever benefit the split provides. One risk worth watching: if a step in the middle of the chain produces unstable output, the error gets carried into the next step unchanged, and can even be amplified further there — so the more critical a handoff point is, the more it's worth pausing for human verification before letting it proceed, rather than letting the whole chain run automatically and only checking at the end.
In its prompt-engineering technical documentation, Anthropic lists prompt chaining as one of the recommended approaches for handling complex tasks, describing how breaking a single large task into several smaller, focused subtasks executed in sequence typically produces more stable, easier-to-debug results than asking a model to complete the entire complex workflow in one call; the approach also shows up widely in real-world automation pipelines chaining together stages like search, summarization, and formatting, and is a common design pattern in prompt engineering for handling multi-step workflows.
The upside is inserting checkpoints at key junctures, so an error only requires fixing a single step rather than redoing the whole process, and each step's output quality becomes easier to verify individually. The downside is that each additional step adds another call and another round of human checking as operational cost — for simple tasks with predictable output, the decomposition itself may not be worth it, and a poorly designed handoff becomes a new source of error planted in the middle of the chain.