RAG
Retrieval-Augmented Generation - A technique that enhances language models by retrieving relevant information from a knowledge base before generating responses.
Core Concepts
Retrieval-Augmented Generation (RAG) is a powerful AI technique that combines the capabilities of large language models with external knowledge retrieval. Instead of relying solely on the model's training data, RAG systems first retrieve relevant information from a knowledge base, then use that information to generate more accurate, up-to-date responses.
How RAG Works
The RAG process involves three key steps:
- Retrieval: When a user asks a question, the system searches a knowledge base for relevant information
- Augmentation: The retrieved information is added to the language model's context
- Generation: The model generates a response based on both its training and the retrieved context
Benefits
- Accuracy: Responses are grounded in actual data rather than model hallucinations
- Up-to-date: Knowledge base can be updated without retraining the model
- Transparency: Can cite sources and show where information came from
- Cost-effective: Cheaper than fine-tuning models for domain-specific knowledge
Common Use Cases
- Question-answering systems over documents
- Technical support chatbots
- Research assistants
- Content recommendation engines
- Enterprise knowledge management
Examples
- •A chatbot using RAG to answer questions based on company documentation
- •Customer support system that retrieves relevant help articles before responding