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Book Overview & Buying
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Table Of Contents
LLMs in Enterprise
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RAG enhances generative AI by integrating real-time external data retrieval, addressing limitations like outdated knowledge and hallucinations through verifiable, attributed sources. It combines sparse (keyword-based) and dense (embedding-driven) retrieval methods, leveraging semantic understanding and efficient indexing (e.g., ANN and HNSW) for relevance. RAG pipelines dynamically synthesize enterprise knowledge via chunking, hybrid retrieval, and context-aware generation while ensuring traceability and governance. By grounding outputs in domain-adapted embeddings and updatable indexes, RAG enables scalable, accurate responses tailored to specialized use cases like technical support or compliance. This approach bridges generative fluency with enterprise needs for transparency, freshness, and auditability.
This chapter explored RAG and its role in enhancing generative AI by integrating external data retrieval. We examined sparse and dense retrieval methods, indexing techniques...