What You Will Learn
- How to distinguish rules, RPA, classical machine learning, LLMs, retrieval-based systems, workflows, and agent-like systems.
- How prompting, retrieval, and system design fit together in a practical business AI stack.
- What AI systems commonly get wrong, and how to think about review, control, and failure handling.
- How to assess cost, latency, ROI, and operating fit before scaling an AI initiative.
- Where to go deeper if you want more technical coverage beyond the scope of this academy.
Lessons in This Section
| Lesson | Focus |
|---|---|
| AI Timeline | A compact historical reference for the major phases, breakthroughs, and shifts that shaped modern AI. |
| LLMs vs Traditional Machine Learning | A practical comparison of large language models and classical machine learning, with guidance on when each approach fits a business problem. |
| Rules, RPA, ML, LLMs, and Agents: The Decision Ladder | A plain-English ladder for choosing the simplest automation approach that actually fits the task. |
| Prompting 101 for Business | A practical guide to writing prompts that produce useful, controlled outputs for real business work rather than clever toy demos. |
| RAG Explained for Business | A business-friendly explanation of retrieval-augmented generation and why it matters when your AI must work from company knowledge. |
| The AI Stack in Plain English | A guided tour of the parts inside a modern AI system, from models and prompts to retrieval, tools, guardrails, and logs. |
| AI Agents vs Workflows | How to separate true agent-like systems from straightforward AI workflows, and why most business use cases should start simpler. |
| What AI Gets Wrong | A practical look at hallucinations, extraction errors, weak reasoning, stale knowledge, and why human review still matters. |
| How to Evaluate an AI Use Case | A practical framework for deciding whether an AI project is worth pursuing, what shape it should take, and how to avoid expensive pilots. |
| Cost, Latency, and ROI of AI Systems | A business-first guide to the trade-offs that determine whether an AI system is merely impressive or genuinely worth operating. |
| Where to Go Deeper Beyond This Academy | A curated reading map for topics this academy does not teach in depth, including transformer internals, fine-tuning, GPU optimization, and benchmarks. |
Suggested Learning Path
Core Path
- AI Timeline
- LLMs vs Traditional Machine Learning
- Rules, RPA, ML, LLMs, and Agents: The Decision Ladder
- Prompting 101 for Business
- RAG Explained for Business
- The AI Stack in Plain English
- AI Agents vs Workflows
- What AI Gets Wrong
- How to Evaluate an AI Use Case
- Cost, Latency, and ROI of AI Systems
Optional Extension
How This Section Is Organized
This section moves from orientation to decision-making.
- Orientation: understand what modern AI is, how it developed, and what kinds of systems now exist.
- Working patterns: learn prompting, retrieval, and workflow design before jumping to “agents.”
- System thinking: understand the AI stack, common failure modes, and why controls matter.
- Business judgment: evaluate use cases based on operating fit, economics, and rollout discipline.
- Further study: use the final lesson as a bridge into more technical material that sits outside this academy’s main focus.
Where to Go Next
- Continue with AI for Business Operations
- Return to the Academy home