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

3 Detecting and Verifying AI Output

Prevention reduces the likelihood of hallucinations, but it does not eliminate them. A model can follow instructions, stay within its context window, and use retrieved sources, but still produce an incorrect answer. Chapter 2, Preventing Hallucinations covered the conditions that lead to hallucinations and how to reduce them before they occur. This chapter focuses on detecting and handling hallucinations that reach the output despite those precautions.

Detection and verification work at two levels. The first is the output itself. This includes techniques for checking what the model produces, comparing it with other sources, and asking the model to examine its own reasoning. The second is the system around ...

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Managing AI Hallucinations

by Arkadiusz Włodarczyk

3

Detecting and Verifying AI Output

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