EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Aug 8·5 min read
Revision Prompting improves industrial LLM processes
TL;DR
Revision Prompting enhances the use of large language models (LLMs) in industrial applications by allowing for both manual and automated prompting. This approach improves tasks like feature implementation and data extraction, streamlining processes significantly.
✦ Why It Matters
Implement Revision Prompting today to streamline your LLM workflows and improve task consistency.
Key Takeaways
- Use Revision Prompting to minimize output inconsistencies.
- Implement patching to reduce processing time and costs.
- Adopt specific formats like POSIXdiff for input changes.
- Expect up to 80% time savings with minimal input changes.
How It Works
Revision Prompting operates by creating a 'RevisionPrompt' that includes the original output and the differences between the original and updated inputs. This allows the LLM to generate a small 'OutputPatch' that only modifies the necessary parts of the output, ensuring consistency and reducing the need for full reprocessing.