Revision Prompting improves industrial LLM processes
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Industrial prompting operates by processing Input data with an Instruction to yield Output. Whenever Input undergoes updates, the same Instruction is re-run on the new Input to generate the updated Output. However, this approach has two significant drawbacks: it re-runs the Instruction on the entire Input and Output, leading to inefficiency and redundancy.
Revision prompting addresses these issues by focusing on input and output revisions rather than the complete Input and Output. Suppose Input has been updated, and you intend to process the updated Input. In that case, Revision Prompting constructs a RevisionPrompt that specifies the changes made to the Input. The LLM then generates an OutputPatch in response to the RevisionPrompt, which is subsequently applied to the original Output to obtain the updated Output.
To illustrate, consider translating a product page of an e-bike to German using an LLM. Later, a battery upgrade extends the range from 80 km to 100 km. Rather than re-translating the entire page, Revision Prompting allows you to prompt the LLM with Applying the OutputPatch to the original Output. The OutputPatch consists of the two lines of text that need updating, significantly less than a full re-translation. Unchanged content remains consistent with the original translation, ensuring efficiency and accuracy.
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