Integrating RAG with graph learning
RAG is an AI framework that combines the power of LLMs with external knowledge retrieval to produce more accurate, relevant, and up-to-date responses. Here’s how it works:
- Information retrieval: When a query is received, RAG searches a knowledge base or database to find relevant information.
- Context augmentation: The retrieved information is then used to augment the input to the language model, providing it with additional context.
- Generation: The LLM uses this augmented input to generate a response that’s both fluent and grounded in the information that’s been retrieved.
RAG offers several compelling perks:
- Improved accuracy: By grounding responses in retrieved information, RAG reduces hallucinations and improves factual accuracy.
- Up-to-date information: The knowledge base can be updated regularly, allowing the system to access current information without having to retrain the entire model...
