Revision Prompting: Cut LLM Processing Costs by 65% Using Input Diffs
Dry_Rabbit_1123 · reddit · 2026-08-11
The author introduces Revision Prompting, a practical technique for automated LLM pipelines where inputs undergo frequent minor changes (e.g., document translation, invoice extraction).
The naive approach of regenerating the entire output whenever the input changes has two flaws: LLM non-determinism rewrites untouched sections, and full generation wastes time. The new method retains the original input/output. When the input updates, you feed the model an input diff and ask it to generate a patch for the original output, which is then applied locally.
Key Results:
- Reduced processing time by 80% and costs by 65%.
- Trades expensive decoded output tokens for cheap prefilled input tokens, offering even greater benefits in local deployments.
- Ensures byte-identical consistency for unchanged segments.
Caveats: If the input changes drastically, a full rerun is recommended. The system must also store historical input/output pairs.
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