Understanding Generative AI and Large Language Models – PDF

VIDEO DEMO

BENEFITS OF THIS DOWNLOADABLE PDF DOCUMENT

1. Demystifies how generative AI and large language models actually work, enabling auditors to assess their risks credibly.
2. Clarifies the real capabilities and limitations of tools such as ChatGPT and Claude for professional audit use.
3. Includes practical templates and a quiz that build genuine, defensible competence in generative AI concepts.

GENAI PDF DESCRIPTION

Generative AI and Large Language Models PDF: Learn transformer architecture, model training, audit risks, and frameworks like IIA, NIST, and ISACA. Download now. Understanding Generative AI and Large Language Models is a 40-page PDF document available for immediate download upon purchase.

Generative AI has moved from novelty to mainstream business tool in record time – and internal auditors are now expected to understand it, audit it, and use it responsibly. This is Module 2 of the AI-Powered Internal Audit Professional Series, a 15-module program developed and published by Business Excellence to give internal audit professionals genuine, practical AI competence.
This module opens the black box of generative AI and large language models. You will learn, in clear professional terms, how transformer architecture and the self-attention mechanism allow modern models to understand context across lengthy documents; how large language models are trained through pre-training, fine-tuning, and reinforcement learning from human feedback; and what a context window is and why it matters when an auditor feeds a model an entire policy or audit report.
Just as importantly, the module is honest about limitations. It explains why models can produce confident but incorrect output, why they do not access real-time databases unless specifically connected to them, and why every factual claim, citation, and statistic a model generates must be independently verified before it enters a workpaper. This balanced, evidence-based treatment is essential for auditors who must neither dismiss the technology nor over-trust it.
The content surveys the major models internal auditors are most likely to encounter – including GPT-4 from OpenAI, Claude from Anthropic, Gemini from Google, and open models such as LLaMA and Mistral – and compares their relevant features, context capacities, and audit-relevant strengths so auditors can make informed tool decisions.
Aligned with the IIA Global Internal Audit Standards 2024, the NIST AI Risk Management Framework, and current guidance from ISACA, the module connects technical understanding directly to audit consequences: what generative AI means for data confidentiality, intellectual property, model risk, and the reliability of AI-assisted work.
Delivered as a professional PowerPoint presentation with full speaker notes, ready-to-use templates, an implementation checklist, real-world examples from cited sources, and a 10-question knowledge assessment quiz, Module 2 builds the conceptual foundation every auditor needs before either auditing or adopting generative AI.

Source: Best Practices in GenAI PDF: Understanding Generative AI and Large Language Models PDF (PDF) Document, Amer Morgan