Retrieval-augmented generation (RAG) is a method that boosts the performance of large language model (LLM) applications by utilizing tailored data.
It achieves this by fetching pertinent data or documents related to a specific query or task and presenting them as context to the LLM.
RAG has demonstrated effectiveness in support chatbots and Q&A systems, especially those that need to stay updated or tap into domain-specific expertise.
- Retrieval-augmented generation (RAG) provides several benefits:
1. Access to Up-to-Date Information: Provides real-time data retrieval for current events.
2. Domain-Specific Knowledge: Integrates specialized documents to enhance expertise.
3. Reducing Model Size: Retrieves relevant information on-the-fly, minimizing the need for huge models.
4. Improving Answer Accuracy: Supplies precise context for more accurate responses.
5. Dynamic Knowledge Integration: Updates information dynamically without retraining.
6. Efficient Resource Utilization: Optimizes computational resources by retrieving only necessary data.
https://www.databricks.com/glossary/retrieval-augmented-generation-rag
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