What is RAG Architecture?
Retrieval-Augmented Generation (RAG) is an AI architecture pattern that combines information retrieval with text generation.
Retrieval-Augmented Generation (RAG) is an AI architecture pattern that combines information retrieval with text generation. Instead of relying solely on a model's training data, RAG systems retrieve relevant documents from a knowledge base and provide them as context for the model to generate more accurate, grounded responses.
Components: Document ingestion pipeline, embedding model, vector database, retrieval engine, reranker (optional), and generation model.
Limitations: RAG retrieves relevant documents but does NOT verify their accuracy. The retrieved document may be outdated, contradictory, or wrong. This is why Exogram's Truth Ledger goes beyond RAG — it verifies facts, not just relevance.
Why It Matters
RAG is the most common architecture for enterprise AI applications. However, RAG without verification creates a false sense of accuracy — the model generates confident, well-sourced answers from potentially incorrect documents.
Frequently Asked Questions
Is RAG enough for production AI?
RAG alone is insufficient for high-stakes applications. RAG retrieves relevant documents but doesn't verify accuracy. For production systems, RAG should be combined with verification infrastructure (like Exogram's Truth Ledger) and governance controls.
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