The hallucination problem
General-purpose AI models are trained on public internet data, not your business. When asked a specific question about your policies or products, they'll often generate a plausible-sounding but incorrect answer.
RAG grounds answers in your data
Retrieval-augmented generation solves this by first searching your actual documents for relevant information, then instructing the model to answer only using what it found — with a citation back to the source.
Why this matters for trust
A support or internal AI system that can point to exactly where an answer came from is dramatically more trustworthy — and auditable — than one that simply asserts an answer with no source.
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