Overview
RAG addresses the limitations of LLMs, such as outdated information (knowledge cutoffs) and the tendency to invent facts (hallucinations), by providing the model with access to up-to-date or domain-specific data.
How it Works
- Retrieval: The system searches external sources (like documents or databases) for information relevant to the user's query.
- Augmentation: The retrieved context is added to the original prompt.
- Generation: The LLM generates a response based on both its training and the provided context.
Benefits
- Reduces factual errors.
- Allows for source attribution.
- Enables real-time knowledge updates without retraining.