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Sources
https://pureai.com/articles/2025/03/03/understanding-rag.aspx
https://learn.microsoft.com/en-us/azure/developer/ai/advanced-retrieval-augmented-generation
https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/
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The Basics of RAG
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Retrieval-Augmented Generation (RAG) enhances AI models by connecting them to external knowledge sources.
- Definition: A technique that combines information retrieval with text generation
- Purpose: Allows AI to access and use information beyond its training data
- Key benefit: More accurate, up-to-date, and personalized responses
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How RAG Works
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- Query processing: User asks a question
- Retrieval: System searches a knowledge base for relevant information
- Context integration: Selected information is fed to the AI
- Generation: AI creates a response using both retrieved information and its knowledge
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Why Use RAG?
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- Overcomes knowledge cutoffs: Accesses current information
- Reduces hallucinations: Grounds responses in retrieved facts
- Handles private data: Works with information not in training data
- Provides citations: Shows sources of information
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RAG vs. Traditional LLMs
- Traditional LLM: "I don't have access to your specific Q1 data."
- RAG-enhanced LLM: "Your Q1 2025 sales were $4.2M, up 12% year-over-year."
Common Applications
- Enterprise search across company documents
- Customer support using product documentation
- Research assistance with specialized literature
- Personal knowledge management
🦥 The Secret Ingredient: Vector Databases
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How does RAG "find relevant info"? It turns text into embeddings (lists of numbers that capture meaning) and stores them in a vector database (Pinecone, Weaviate, Qdrant, Chroma, pgvector). At query time it finds the chunks whose meaning is closest to your question, not just keyword matches.
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🤖 Agentic RAG
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Basic RAG retrieves once, then answers. Agentic RAG lets the agent decide when to search, what to search for, search multiple times, and even pick which knowledge base to use. Much better for complex, multi-part questions.
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RAG represents a major advancement in making AI systems more reliable and useful by grounding them in specific, relevant information tailored to your needs.