AI Dictionary › Prompting
Scomposizione dei Compiti
Task decomposition is the prompting technique of breaking a complex task into a sequence of smaller, manageable sub-tasks to be tackled one at a time rather than as a single undifferentiated block. Models work better on narrowly scoped units of work than on broad, composite requests.
The prompt makes the structure of the problem explicit in distinct phases: gathering the necessary information, defining criteria, drafting, verifying the result. Each phase can be handled in a separate turn or within the same prompt but with clear sequential instructions, so the model does not have to hold every aspect of the task in mind at once.
In practical use, task decomposition is central when automating articulated processes: generating a report means first collecting the data, then structuring it, then writing it in prose; planning a project means first defining objectives, then phases, then required resources. It is also the principle behind many AI agent pipelines, where a task is divided among multiple steps or specialized agents.
The concept derives from the problem decomposition typical of software engineering and project management, applied to prompting as it was observed that language models achieve more reliable results when complexity is spread across multiple steps rather than concentrated in a single instruction.
Grace assesses whether you can break a complex scenario into ordered steps instead of asking the model to solve everything in a single undifferentiated prompt.
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