[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODQxODk","title","Building Hallucination-Resistant RAG and AI Agents","state","published","contactAuthorMethod","email","aboutText","

Large language models hallucinate with startling confidence. In high-stakes domains like healthcare, law, finance, and operations, a single fabricated statistic or invented citation can cause real harm. This book shows you how to build retrieval-augmented generation (RAG) systems and AI agents that know when they have evidence, verify what they generate, detect when they are uncertain, and abstain rather than bluff when information is insufficient. We move beyond the myth of zero hallucinations and focus on what is actually achievable: layered, defense-in-depth architectures that reduce hallucination rates to the lowest level compatible with your latency, cost, and complexity constraints.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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