Foundations of AI

A practical framework for understanding the economic trade-offs of AI systems, including model cost, response speed, review effort, and business payoff.

A practical decision ladder for choosing between rules, RPA, traditional machine learning, LLM workflows, and agent-like systems.

A plain-English guide to the main layers of a modern AI system, from models and prompts to retrieval, tools, guardrails, and review.

A practical guide to the most common ways AI systems fail in business settings, and how to design review controls before those failures become operational problems.

A curated guide to textbooks, authors, websites, and papers for readers who want to study transformer internals, attention math, fine-tuning, GPU optimization, and benchmarking in more depth.

How to separate true agent-like systems from straightforward AI workflows, and why most business use cases should start simpler.

A practical framework for deciding whether an AI project is worth pursuing, what shape it should take, and how to avoid expensive pilots.

A practical comparison of large language models and classical machine learning, with guidance on when each approach fits a business problem.

A practical guide to writing prompts that produce useful, controlled outputs for real business work rather than clever toy demos.

A business-friendly explanation of retrieval-augmented generation and why it matters when your AI must work from company knowledge.