RAG (Retrieval-Augmented Generation)
What is RAG (Retrieval-Augmented Generation)?
RAG is an AI technique that combines information retrieval with text generation. It first searches through documents or databases to find relevant information, then uses that information to generate more accurate and up-to-date responses. This matters because it helps AI systems provide factual answers based on current knowledge rather than just what they learned during training.
Technical Details
RAG systems typically use dense vector embeddings and similarity search algorithms like FAISS or ANN to retrieve relevant documents, then feed this context along with the original query into a large language model for generation. The architecture separates retrieval and generation components, allowing for modular updates to knowledge bases.
Real-World Example
When you ask ChatGPT about recent news events, it uses RAG to search through current news articles and then generates responses based on that retrieved information, ensuring you get up-to-date answers rather than outdated training data.
AI Tools That Use RAG (Retrieval-Augmented Generation)
ChatGPT
AI assistant providing instant, conversational responses across diverse topics and tasks.
Claude
Anthropic's AI assistant excelling at complex reasoning and natural conversations.
Perplexity AI
AI-powered search engine providing instant answers with cited sources for accurate information.
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