Prompt chaining

A single prompt demanding too much at once easily overwhelms a model: it overlooks sub-tasks, mixes steps or delivers an overall result that is hard to control. Prompt chaining counters that by breaking the task into clearly defined stages. Each stage has an unambiguous goal, and its result can be checked before things continue.

This breaking-up brings several advantages. The individual steps are simpler and therefore more reliable, errors are easier to locate, and logic, validation or external data sources can be built in between the steps. Instead of an opaque black box, a traceable process with defined intermediate states emerges.

Prompt chaining is therefore a basic building block of more demanding applications. It bridges the single prompt and more complex processes and is closely related to the step-by-step thinking models do internally in chain of thought — except that here the steps are explicitly and controllably separate.

Several basic patterns have emerged in practice. In the sequential pattern the steps run strictly one after another, each building on the last. In the branching pattern an intermediate result decides which of several possible next steps is carried out. In the iterative pattern a step is repeated until a result meets a quality criterion.

The table below summarises these patterns. In real applications they often appear combined: a largely sequential chain can branch at one point and iterate at another. Which pattern fits follows from the structure of the task and the degree of control needed.

Every transition in the chain has to be cleanly defined. One step's output format should match the next one's expected input format so the handover works reliably. Clear, structured intermediate output — as a list or a clearly delimited block for instance — makes chains more robust.

Take as an example the task of turning a long piece of customer feedback into a short, friendly draft reply. Instead of asking for that in one prompt, you break it into clear steps whose intermediate results can each be checked. That makes the outcome more reliable than one sweeping attempt.

The steps below show a typical sequential process. Each step takes the previous one's result as input and has a clearly defined goal. That creates a traceable chain ending in a checked draft.

The effort of breaking things down pays off above all with recurring, demanding processes. Once cleanly defined, such a chain can be applied again and again as a template and improved at individual points without rethinking the whole process.

Prompt chaining is not always the best choice. Every extra stage means another model call and so more latency and cost. For simple tasks that a single well-worded prompt solves reliably, breaking them up does not pay and only complicates the process needlessly.

Errors can also propagate along the chain: if an early step gives a faulty intermediate result, the next builds on it. Validation at critical transitions therefore matters, and the steps should be cut so errors show up early and can be corrected.

Prompt chaining differs from the related notion of agents in that the sequence of steps here is generally set by the developer. With autonomous agents, by contrast, the model itself decides dynamically on the next steps. Prompt chaining is therefore the more controlled, more predictable approach and often the right first step before moving to more open architectures.

For production use it is advisable to document a proven chain as a reusable template and improve individual steps independently. Bottlenecks can thus be tackled deliberately without redesigning the whole process. Logging the intermediate results helps too, because when something goes wrong it shows immediately at which point in the chain the problem arose and speeds up debugging considerably.