RAG (Retrieval-Augmented Generation)
A technique that combines information retrieval with language generation, allowing AI models to access external knowledge bases and provide accurate, up-to-date answers grounded in specific documents.
Detailed Explanation
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language models by connecting them to external knowledge sources. When a user asks a question, RAG first retrieves relevant documents from a database (using semantic search), then feeds those documents to the LLM as context for generating a response. This approach solves key LLM limitations: hallucination (making up facts), outdated knowledge (training data cutoff), and inability to access proprietary data. RAG enables businesses to build AI assistants that answer questions based on their own documents, manuals, databases, or real-time information, ensuring accuracy and relevance without expensive model retraining.
Real-World Examples
Enterprise Knowledge Base
Enterprise SoftwareCompanies build RAG systems that answer employee questions by retrieving information from internal wikis, policies, and documentation, reducing time spent searching for information by 70% and improving onboarding efficiency.
Customer Support Assistant
E-commerceE-commerce platforms use RAG to create chatbots that pull information from product manuals, FAQs, and order histories to provide accurate, personalized support, resolving 65% of inquiries without human intervention.
Research Assistant
ResearchResearchers use RAG systems to query vast scientific literature, retrieving relevant papers and generating summaries with citations, accelerating literature reviews from weeks to hours.
Frequently Asked Questions
Q:How is RAG different from fine-tuning?
Fine-tuning modifies the model's weights to learn new patterns (expensive, requires retraining for updates). RAG keeps the model unchanged but provides it with relevant context at query time (cheaper, instantly updatable). RAG is better for frequently changing information; fine-tuning for learning new behaviors or styles.
Q:What's the best vector database for RAG?
Popular choices include Pinecone (managed, easy), Weaviate (open-source, feature-rich), Chroma (lightweight, Python-friendly), and Qdrant (fast, scalable). Choice depends on scale, budget, and technical requirements. Most offer free tiers for experimentation.
Related Terms
Large Language Model (LLM)
AI models trained on vast amounts of text data that can understand and generate human-like text, powering applications like ChatGPT, content generation, and code assistance.
Embedding
A numerical representation of data (text, images, etc.) in a continuous vector space where similar items are positioned close together.
Vector Database
A specialized database designed to store and efficiently search high-dimensional vector embeddings, enabling semantic search and similarity matching at scale.
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