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How Retrieval-Augmented Generation delivers 94% answer accuracy on enterprise documents—processing 10M+ queries monthly with $7M operational savings
Retrieval-Augmented Generation combines document retrieval with generative AI, enabling LLMs to answer questions using enterprise knowledge bases with verifiable sources and 94% accuracy—eliminating hallucinations that plague pure LLM approaches.
| Metric | Traditional Search | RAG System | Improvement |
|---|---|---|---|
| Answer Accuracy | 62% | 94% | +52% |
| Response Time | 5-15 min | 3-8 sec | 98% faster |
| User Satisfaction | 72% | 91% | +19 points |
| Cost per Query | $2.50 | $0.08 | 97% reduction |
Test chunk sizes (256-1024 tokens) with overlap (10-20%) for optimal retrieval vs context balance.
Combine semantic (vector) search with keyword (BM25) search for 15-25% accuracy improvement.
Use cross-encoder models (Cohere rerank, BAAI reranker) to refine top-K results before LLM generation.
Track accuracy, latency, user feedback; A/B test embedding models, chunk sizes, and retrieval parameters.
AI agents autonomously decide when to retrieve, which sources to query, and how to combine information for complex multi-step reasoning tasks.
Models self-reflect on retrieved content quality and generated answers, automatically refining searches and regenerating improved responses.
Knowledge graphs enhance retrieval by capturing relationships between entities, enabling multi-hop reasoning and contextual understanding.
Deploy production-grade RAG solutions that unlock your enterprise knowledge and deliver accurate, verifiable answers at scale.
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