How RAG Grounds h2oGPTe Answers in Your Documents
- Generative AI
- Enterprise h2oGPTe
This lesson shows retrieval-augmented generation in action inside Enterprise h2oGPTe by asking a question tied to a document uploaded to a collection. The response comes back rephrased and summarized from the source material rather than from the model's general knowledge, and a References button reveals exactly which pages in the document were used, along with a relevance score for each. This demonstrates that the platform is grounding its answer in the trusted uploaded source rather than fabricating information, and shows how uploading domain-specific documents improves the relevance and accuracy of the responses you get back.
Key takeaways
- How a RAG-based response draws its answer from an uploaded document instead of general model knowledge
- What the References button reveals about which document pages were used to generate an answer
- How a relevance score attached to each referenced page indicates how strongly it matched the question
- Why grounding answers in an uploaded document helps avoid fabricated or inaccurate information
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