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Sintesi Progressiva del Prompt
Progressive summarization prompting is a technique for handling texts too long to process in a single pass: the document is divided into smaller chunks, each summarized separately by the model, and the partial summaries are then combined and further synthesized until a coherent final result of the desired length is obtained.
The typical process proceeds in stages: the original text is split into chunks that fit within the model's context window, each chunk is summarized independently, the partial summaries are concatenated and put through a further synthesis pass that integrates them into a single coherent text, possibly repeating the process across multiple levels for very long documents.
This technique is essential when processing documents that exceed the available context window, for example summarizing an entire book, a long transcript, or a set of related documents, where it is not possible to fit all the text into a single prompt and one must therefore work through successive approximations that preserve the most relevant information.
The term describes the incremental, hierarchical nature of the process, an approach that became established as the use of language models extended to documents whose length systematically exceeds the limits of the context window available in a single pass.
Grace offers scenarios with long documents where you must decide how to split the information and then recombine it into a coherent final summary.
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