book

Managing AI Hallucinations

Name: Managing AI Hallucinations
Author: Arkadiusz Włodarczyk
ISBN: 9781808087936

by Arkadiusz Włodarczyk

September 2026

98 pages

2h 20m

English

Packt Publishing

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Preface
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Chapter 1: Understanding AI Hallucinations
How AI hallucinations occurHow LLMs generate textWhat are AI hallucinations?Assessing business risks and recognizing AI errorsEvaluating the business risks of unchecked AI outputHallucinations and incorrect informationSecurity risksPrivacy, bias, and accountabilityRecognizing common hallucination patternsDetecting fabricated factsIdentifying fabricated referencesEvaluating overconfident examplesSummary
Chapter 2: Preventing Hallucinations
Technical RequirementsControlling AI output through prompt designGuiding AI with instructions, guardrails, and contextCrafting clear, specific, constrained promptsUsing few-shot prompting and chain-of-thought for reliable responsesUsing AI to improve your promptsPreventing hallucinations through RAGUnderstanding RAG and how it reduces hallucinationsUsing NotebookLM as an instant RAG databaseHow model selection affects hallucination rateUsing deep research for reliable informationManaging context length to avoid hallucinationsUnderstanding what happens when the limit is exceededManaging context effectivelyRecognizing context-limit hallucinationsBuilding your own local RAG databaseSetting up the projectUsing Codex or Claude CodeSetting up manuallyUnderstanding how the RAG system worksSummary
Chapter 3: Detecting and Verifying AI Output
Technical RequirementsApplying verification techniquesCross-checking multiple modelsFact-checking with external sources and APIsUsing self-ask and self-consistency promptsBuilding a verification workflowDesigning AI systems for reliabilityUsing AI-powered code review toolsDesigning AI systems with safety guardrailsUsing guardrails to limit riskScanning model outputUsing logs to track failuresUsing fallbacks for model failuresBuilding a minimum safety kitScreening input with a moderation APIBuilding a knowledge-based AI agentUsing file search and multi-step workflowsBuilding more complex workflowsManaging model usage and costsDividing complex tasksSummary
Chapter 4: Deploying AI Responsibly
Technical Requirementshttps://github.com/PacktPublishing/Managing-AI-Hallucinations/tree/main/Chapter04Addressing ethical and safety considerationsBias, misinformation, and compliance risksChecking for biasWhen to escalate to human reviewCommunicating AI limitations to teamsMonitoring LLMs in productionMonitoring LLMs with OpenTelemetry, Prometheus, and GrafanaMonitoring is not hallucination detectionSetting up an LLM hallucination monitorUnderstanding the LLM hallucination monitoring stackSummary
Chapter 5: Unlock the Code Bundle and the PDF Version
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Index

Content preview from Managing AI Hallucinations

Preface

Large language models can produce fluent and convincing answers while still being wrong. A fabricated citation, an invented API, an unsupported factual claim, or an incorrect piece of generated code can appear plausible enough to go unnoticed. This book focuses on reducing the gap between fluency and reliability.

Instead of treating hallucinations as a single prompt-engineering problem, this book approaches AI reliability as a process. You will learn why hallucinations occur, how to reduce them before they happen, how to verify AI-generated output, and how to design systems that detect and handle failures before they reach users. The book combines practical prompting techniques with retrieval-augmented generation (RAG), source-based ...

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ISBN: 9781808087936

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Managing AI Hallucinations

by Arkadiusz Włodarczyk

Preface

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.