Foundations of AI

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.