WHITE PAPER
Closing the Confidence Gap
Conversational AI lets visitors ask a complete question and get a direct answer — no more guessing keywords or digging through search results. But a generated answer can sound polished even when the evidence behind it is incomplete, outdated, or only loosely related to the question.
That's the confidence gap: the distance between how reliable an answer appears and how well the evidence behind it actually supports that confidence. This practical guide breaks down what has to happen — before, during, and after retrieval — to close it.
That's the confidence gap: the distance between how reliable an answer appears and how well the evidence behind it actually supports that confidence. This practical guide breaks down what has to happen — before, during, and after retrieval — to close it.
- Why grounding a model in your content isn't the whole job — and what else has to go right
- The chain of decisions between a visitor's question and a trustworthy answer
- How to know when your evidence foundation is available, findable, and sufficient to support a response
- When to generate an answer — and when to fall back to search results, a clarifying question, or a human expert
- Why one shared retrieval foundation can serve every site, audience, and journey without creating separate evidence silos
- How to measure and maintain trust after launch, not just at launch
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