Introduction
Retrieval-Augmented Generation (RAG) is a framework that enhances large language models (LLMs) by combining them with external knowledge retrieval systems. This approach allows LLMs to access and leverage specific information from dedicated knowledge bases while generating responses.
Key Features
- Knowledge Retrieval: Ability to query vector databases for relevant information based on user input.
- Context Integration: Seamlessly combines retrieved information with the LLM's general knowledge.
- Text Generation: Produces coherent responses incorporating both retrieved and model knowledge.
- Vector Embeddings: Converts queries and documents into vector representations for efficient similarity search.
Advantages
- Reduces hallucination by grounding responses in specific knowledge sources.
- Enables access to up-to-date information through knowledge base updates.
- Maintains privacy by keeping sensitive data in controlled environments.
Use Cases
Traditional RAG finds applications in various domains:
- Documentation: Query technical documentation, user manuals, and knowledge bases for accurate information retrieval.
- Research: Access and synthesize information from academic papers and research documents.
Summary
Traditional RAG combines the power of LLMs with external knowledge retrieval, enabling more accurate and informed responses. This approach has become fundamental in creating reliable AI systems that can access and utilize specific information while maintaining the natural language capabilities of LLMs.