# Revision Prompting: improves industrial LLM processes > Source: > Published: 2026-08-12 11:58:05+00:00 ## The Problem that Revision Prompting solves We prompt LLMs in two ways: - Ad-hoc prompting - Prompts LLMs manually, with a custom instruction per call. ## Examples - Asking a coding agent to implement a new feature. - Asking a chatbot to draft an email. - Industrial prompting - Prompts LLMs as part of an automated process, with the same instruction across calls. ## Examples - Extracting structured information from invoices as part of an accounting pipeline. - Translating documentation pages into other languages as part of a release process. Industrial prompting typically processes some `Input` data with an `Instruction` to produce some `Output`. Whenever the `Input` gets updated, industrial prompting naively re-runs the `Instruction` on the `UpdatedInput` to produce the `UpdatedOutput`. This approach has two downsides: - Lack of consistency - LLMs are non-deterministic, so the `UpdatedOutput` differs from the original`Output` beyond what the`UpdatedInput` necessitates. - Full processing time and token costs - Although only parts of the input have changed, we produce the `UpdatedOutput` in full. This is as expensive as the production of the original`Output`. *Revision prompting* resolves both downsides of naive re-runs by operating on the input and output **revisions** instead of the full input and output. ## How Revision Prompting works Assume you have processed some `Input` with an `Instruction` to produce some `Output` by prompting an LLM with `Instruction`: `Input` Now, `Input` has been updated, and you also want to process the `UpdatedInput`. Revision Prompting processes the `UpdatedInput` by constructing the `RevisionPrompt` as `Instruction`: `Input` produced `Output`. The input got updated as follows: `diff(` .`Input`, `UpdatedInput`) Please produce a patch to update the output. The LLM responds to the `RevisionPrompt` with the `OutputPatch` that we apply to the `Output` to obtain the `UpdatedOutput`. ## Example You translate the product page of an e-bike to German with the prompt `The Vela 3 e-bike has a range of 80 km.` Its battery recharges in three hours. The frame is made from recycled aluminium. Every Vela 3 includes a two-year warranty. and the LLM produces `Das E-Bike Vela 3 hat eine Reichweite von 80 km.` Sein Akku lädt in drei Stunden auf. Der Rahmen besteht aus recyceltem Aluminium. Jedes Vela 3 hat zwei Jahre Garantie. Later, a battery upgrade increases the range from 80 km to 100 km. Instead of re-translating the whole page, you prompt `The Vela 3 e-bike has a range of 80 km.` Its battery recharges in three hours. The frame is made from recycled aluminium. Every Vela 3 includes a two-year warranty. produced Output`Das E-Bike Vela 3 hat eine Reichweite von 80 km.` Sein Akku lädt in drei Stunden auf. Der Rahmen besteht aus recyceltem Aluminium. Jedes Vela 3 hat zwei Jahre Garantie. The input got updated as follows: diff(Input, UpdatedInput)- The Vela 3 e-bike has a range of 80 km.+ The Vela 3 e-bike has a range of 100 km. Please produce a patch to update the output. The LLM responds with Applying the `OutputPatch` to the original `Output` produces the updated translation. The `OutputPatch` contains only two lines of text instead of a full re-translation. Unchanged content stays consistent with the original translation. ## Why Revision Prompting works - Consistency - By supplying the LLM with the input revision `diff(` , we ensure that the`Input`,` UpdatedInput`)` OutputPatch`is limited to what the input changes necessitate. Everything not touched by the`OutputPatch` remains identical to the original`Output`. Therefore, the` UpdatedOutput`is**consistent** with the original` Output`. - Time & cost savings - Revision Prompting feeds the original `Output` back in as part of the prompt, so the LLM only generates the short`OutputPatch`. Most tokens thereby move from the output to the input. Since processing time scales roughly with the length of the output, this eliminates most of the processing time. It also converts most of the output token cost into much cheaper input token cost. If the re-run happens within a couple of minutes of the original run, prompt caching reduces part of the input token cost as well. ## Revision Prompting in practice - Revision Formats - The ideal formats for encoding `diff(` and the`Input`,` UpdatedInput`)` OutputPatch`depend on the` Instruction`. The[POSIX](https://pubs.opengroup.org/onlinepubs/9799919799/utilities/diff.html)is a useful generic format. For JSON outputs, the`diff` utility[JSON Patch](https://jsonpatch.com/)format works well. - Expected Savings - The time & cost reduction scales with the size of the input changes and the sensitivity of the output to changes in the input. In our own industrial prompts, Revision Prompting reduces time by ~80%, and costs by ~65%.