Mastering the Next Generation of AI Prompt Engineering for Internal Audit
From Advanced Prompting to Agentic AI, Deep Research and AI-Powered Audit Workflows
Prerequisite/ Requirement: Completion of Parts 1 and 2 of the Audit Training courses, or passing our individual ARC front-up assessment test before the start of the training course
Suggested training duration: Three half‑day sessions (total 1.5 days)
Artificial Intelligence is evolving rapidly, and so is the way Internal Auditors need to work with it. The latest generation of AI models like ChatGPT capabilities is moving far beyond simple question-and-answer interactions. Advanced reasoning, large-context analysis, Deep Research, persistent Projects, connected data sources and increasingly agentic workflows are fundamentally changing what auditors can delegate to AI. As a result, professional Prompt Engineering is evolving from writing instructions into the systematic design of AI-supported audit tasks.
For Internal Audit professionals, this means that Prompt Engineering is becoming a strategic capability. The question is no longer simply how to formulate a good prompt. The real challenge is to understand how to structure a complex audit task, select the appropriate reasoning level, provide the right context, define evidence requirements, control the workflow and determine where human judgment must remain in the loop.
This advanced training provides an overview of the latest developments in teamwork with AI and translates them into practical Prompt Engineering techniques for Internal Audit. Participants will learn how to move from traditional prompting toward advanced approaches such as reasoning-effort prompting, plan-first prompting, context engineering, evidence-pack prompting, trusted-source research, prompt chaining, self-review and AI quality gates.
Learning Objectives
By the end of the training, participants will be able to:
- Distinguish between traditional prompting, advanced Prompt Engineering and AI workflow design.
- Select an appropriate reasoning level for different audit tasks.
- Design prompts using advanced techniques such as plan-first prompting, context engineering and prompt chaining.
- Understand the latest developments in ChatGPT and their relevance for Internal Audit.
- Structure large evidence packages and complex audit contexts for AI-supported analysis.
- Design Deep Research prompts using source hierarchies, trusted sources and explicit research criteria.
- Apply outcome-based prompting to complex multi-step audit tasks.
Audit Training Agenda
1. Introduction – The New AI Reality for Internal Audit
- From GenAi, e.g. ChatGPT as a tool to AI as a working environment
- Why Prompt Engineering is evolving
- From individual productivity to AI-enabled audit workflows
- The changing role of the auditor in an increasingly agentic environment
2. AI Challenge – Test Your AI Knowledge
A dynamic and competitive Game to activate existing knowledge, identify learning gaps and introduce key concepts in an engaging way.
- Category 1 – AI Foundations & Key Terminology
- Category 2 – LLMs & Their Tells
- Category 3 – The Prompting Frameworks
- Category 4 – Advanced Prompting Techniques & Logic
- Category 5 – Agentic AI & External Data Integration
3. Advanced Reasoning – Prompting the “Thinking” Process
- Choosing the appropriate reasoning effort
- Matching reasoning intensity to audit complexity
- Plan-first prompting
- From “give me an answer” to “develop, challenge and validate the approach”
4. Context Engineering – The New Core of Prompt Engineering
- From clever prompts to high-quality context
- Working with large evidence packages
- 256K-token context and what it means for auditors
5. Projects, Memory and Persistent Audit Context
- Building a persistent audit knowledge environment
- Source once, reuse many times
- Maintaining methodology, prior analyses and reusable knowledge
6. Deep Research for Internal Audit
- Designing a research strategy instead of simply asking a question
- Trusted-source prompting
- Regulatory and supervisory research
7. From Prompting to Workflow Engineering
- Outcome-based prompting
- Defining goals, constraints, deliverables and acceptance criteria
- Prompt chaining versus the “Mega-Prompt”
8. From AI Assistant to AI Audit Workforce
- AI Agent prompting
- Specialised audit agents along the audit value chain
9. Multi-Agent Prompting & the Future Audit Workflow
- AI workforce orchestration
- Combining research, analysis and QA agents
- The future role of the auditor as AI Engagement Manager
10. From Training to Implementation
- Which techniques can be implemented immediately?
- Which audit processes are best suited for advanced prompting?
- Quick wins versus strategic AI initiatives
- Personal action plan
- Key takeaways
