Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique that enhances language models by retrieving relevant information from external knowledge sources and incorporating it into the generation process. This allows LLMs to access up-to-date or domain-specific information beyond their training data.
How RAG Works
- Query Processing: The user’s query is processed and analyzed
- Retrieval: Relevant documents or information are retrieved from a knowledge base
- Context Enhancement: The retrieved information is combined with the original query
- Generation: The enhanced context is sent to the LLM, which generates a response
Benefits of RAG
- Accuracy: Reduces hallucinations by grounding responses in factual information
- Up-to-date Knowledge: Provides access to information beyond the model’s training cutoff
- Domain Expertise: Can incorporate specialized knowledge from specific domains
- Transparency: Sources can be cited, making the system more trustworthy and verifiable
Components of a RAG System
- Document Store: A database or vector store containing the knowledge base
- Retriever: A system that finds relevant documents based on queries
- Generator: The LLM that produces the final response
- Orchestrator: Coordinates the flow between components
Use Cases
- Question answering systems
- Customer support chatbots
- Research assistants
- Documentation search tools
- Educational applications