AI for Business Professionals

Machine Learning Is Pattern-Finding, Automated

Instead of a person writing the rules, the machine finds the patterns itself by working through a large pile of data. There are three basic flavors: supervised learning (learn from matched input-output pairs), unsupervised learning (find structure in data nobody labeled), and reinforcement learning (try things, keep what earns a reward). ChatGPT is a very large supervised model — what it learned from all that text is the probability of the next token.

Overfitting and Generalization: Knowing Where AI Breaks

A model can fit its training data perfectly and still fall apart on anything new. That failure is called overfitting, and it's the single biggest risk in an AI project. The standard defense is to hold back part of your data and score the model only on what it has never seen. In business you meet this as the model that worked beautifully on internal data and was useless on real customers. Whenever someone hands you an accuracy number, ask one question first: is that on held-out data, or on the data it trained on?

The Math Behind Prompt Engineering

A prompt is really a set of conditions on a probability. Give the model a role, the background it needs, a few worked examples, and the output format you want, and you narrow that probability distribution toward the answers you're after. Chain-of-thought prompting — asking the model to show its intermediate steps — works for the same reason: each step it writes becomes another condition sharpening the next one. Once you see prompting as structure rather than intuition, you get better at it much faster.