What is Retrieval-Augmented Generation?
Retrieval-Augmented Generation (RAG) is a technique that enhances large language model (LLM) responses by first retrieving relevant documents from a knowledge base, then using those documents as context for the model's response generation.
Retrieval-Augmented Generation (RAG) is a technique that enhances large language model (LLM) responses by first retrieving relevant documents from a knowledge base, then using those documents as context for the model's response generation.
How RAG works: 1. User sends a query 2. The query is converted to a vector embedding 3. Similar documents are retrieved from a vector database 4. Retrieved documents are included in the LLM prompt as context 5. The LLM generates a response grounded in the retrieved documents
RAG reduces hallucination by grounding the model's response in factual source material rather than relying solely on the model's training data.
Why It Matters
RAG is the most widely deployed technique for making AI systems more accurate and trustworthy. However, RAG alone is insufficient — it does not guarantee that the retrieved documents themselves are correct, current, or non-contradictory.
Exogram's Truth Ledger goes beyond RAG by ensuring that the underlying knowledge base is versioned, source-attributed, conflict-checked, and temporally valid. RAG answers "what documents are relevant?" — the Truth Ledger answers "are those documents true?"
How to Measure
Track retrieval precision (percentage of retrieved documents that are relevant), response accuracy (percentage of responses that are factually correct), and hallucination rate (responses that contradict retrieved documents).
Frequently Asked Questions
Does RAG eliminate hallucinations?
No — RAG reduces hallucinations but does not eliminate them. The model can still ignore retrieved context, hallucinate beyond the context, or retrieve outdated/incorrect documents. A truth verification layer (like Exogram) is needed for high-stakes use cases.
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