TOPIC HUB
RAG (Retrieval Augmented Generation)
Deep dive into RAG systems that ground AI in your knowledge base for accurate, cited responses. Learn implementation strategies, best practices, and real-world applications.
RAG enhances LLMs by retrieving relevant context from knowledge bases before generating responses. Architecture: document chunking → embedding → vector search → reranking → context injection → generation. Reduces hallucinations, enables source citation, and allows real-time knowledge updates without retraining.
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RAG Fundamentals
How RAG works, why it matters, and when to use it over fine-tuning.
RAG in Practice
Real-world implementations in customer experience and support.
Latest RAG Articles
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