What Is RAG and Why It Matters
- Generative AI
- Enterprise h2oGPTe
This lesson walks through what retrieval augmented generation is and why it matters, contrasting a static large language model with one that retrieves current facts before generating a response. It covers how RAG combines a retriever, which pulls relevant chunks from public or private sources, with a generator that turns those chunks into a coherent answer. It walks through examples across finance, healthcare, banking, and insurance, showing how RAG supports faster compliance checks, KYC onboarding, and fraud detection. It closes by connecting these use cases back to Enterprise h2oGPTe's RAG capabilities, including VectorDB and embeddings.
Key takeaways
- What separates a plain large language model from a RAG model, which retrieves fresh information before answering
- How RAG combines a retriever, pulling relevant chunks, with a generator that writes the final response
- Why financial analysts and private equity teams use RAG to merge public filings with private due diligence data
- How RAG speeds up bank KYC onboarding by combining public regulatory data with private customer records
- The insurance use case shows RAG comparing private claims against public fraud alerts to catch subtle patterns
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