Managing AI Hallucinations: Detect, prevent, and verify AI hallucinations using RAG, prompt guardrails, and safer LLM workflows - Couverture souple

Arkadiusz Włodarczyk

Couverture souple

ISBN 10 : 1808087933 ISBN 13 : 9781808087936

Editeur : Packt Publishing, 2026

Synopsis

Reduce risk from unreliable AI outputs by learning how to detect hallucinations, verify claims, ground responses with RAG, design prompt guardrails, and monitor LLM workflows before flawed answers reach users or decisions.

Key Features

• Detect fabricated facts, weak citations, outdated claims, and unsafe AI outputs before they create risk
• Use prompt guardrails, RAG, NotebookLM-style grounding, and model checks to improve LLM reliability
• Apply fact-checking, escalation, logging, and monitoring workflows for safer AI adoption

Book Description

LLMs now draft content, review code, answer customers, summarize research, and support decisions, but fluent output can still be false, outdated, biased, or hard to trace. This book helps readers reduce that risk by treating hallucinations as a reliability problem, not a vague AI limitation.

You will learn how LLMs generate responses and why prediction-based systems can produce fabricated facts, weak citations, broken logic, and overconfident explanations. You will then spot hallucination patterns, define trust boundaries, and decide when an answer should be accepted, checked, or rejected. The book then moves into prevention and verification. You will design clearer prompts, system instructions, constraints, examples, self-checking prompts, and guardrails that reduce ambiguity. You will also use Retrieval-Augmented Generation, source grounding, external references, APIs, and model comparison to verify claims against trusted evidence.

Later chapters apply these techniques in real workflows, including AI-assisted coding, research, business analysis, agents, fallbacks, human review, logs, and monitoring with OpenTelemetry, Prometheus, and Grafana. By the end, you will be able to build safer LLM workflows that make unreliable outputs easier to detect, prevent, verify, and monitor before they affect users or decisions.

What you will learn

• Explain why LLMs hallucinate and where reliability failures appear
• Detect fabricated facts, weak citations, outdated claims, and flawed code
• Create prompt constraints, system instructions, examples, and guardrails
• Ground LLM responses using RAG, trusted sources, APIs, and NotebookLM-style workflows
• Verify claims through source checks, self-checking prompts, and model comparison
• Apply safer AI review, escalation, fallback, and human-in-the-loop processes
• Monitor LLM workflows with logs, OpenTelemetry, Prometheus, and Grafana

Who this book is for

This book is for data scientists, AI engineers, developers, technical leads, product managers, compliance professionals, and business teams adopting LLMs in code, content, research, analytics, or decision-support workflows. It is especially useful for readers who need practical methods to reduce AI hallucinations, validate outputs, and communicate AI limits clearly. No advanced programming experience is required, but a basic understanding of LLM tools like ChatGPT, plus familiarity with APIs, JSON, or command-line workflows, will help with the technical examples and exercises.

Table of Contents

1. Understanding AI Hallucinations
2. Preventing Hallucinations
3. Detecting and Verifying AI Output
4. Responsible Deployment

A propos du livre

À propos de l'auteur

Arkadiusz Włodarczyk is a programming instructor, developer, and online course creator with 20+ years of experience in programming and web development. He has created 27 video courses on programming, web development, and mathematics, with more than 350,000 students enrolled on Udemy. His recent work focuses on practical AI-assisted development, prompt engineering, and safer LLM workflows. He has used AI coding tools to build real software, including a multi-tenant SaaS platform for preschool management, an e-invoicing system, a computer game, product visualization tools, and business automation systems. He is also the creator of AI Agents for Codebase Auditing, a collection of 50+ agents for reviewing codebases.

Détails de l'édition

• Éditeur: Packt Publishing
• Date d'édition: 2026
• Langue: anglais
• ISBN 10: 1808087933
• ISBN 13: 9781808087936
• Reliure: Copertina flessibile
• Numéro d'édition: 1
• Nombre de pages: 63
• Coordonnées du fabricant: non disponible
• Personne responsable: gpsr@libri.de; gpsr@libri.de; ; Europaallee 1; Bad Hersfeld; 36244; Allemagne