<ul><li>AI systems, specifically Retrieval-Augmented Generation (RAG) systems, sometimes create a gap between sounding right and being trustworthy where hallucinations hide.</li><li>Building AskNeedl, a RAG-based intelligence layer, involved identifying subtle failure patterns, establishing user trust through citations, and implementing structured QA and feedback loops.</li><li>Enterprise users seek fact-grounded answers rather than inference-driven summaries, highlighting the importance of citation design for user adoption.</li><li>Common challenges encountered include temporal drift in responses, missing multi-document reasoning, weak anchoring in list-type responses, ambiguous queries leading to overgeneralization, and missing citations in outputs.</li></ul>

When AI Sounds Right But Isn’t Enough

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