5-Minute Lessons

Bytesized AI

Short slide decks explaining AI concepts in plain English — designed for legal professionals with no technical background. Click any episode to view it right here.

11 episodes · use arrow keys to navigate slides

Episode 17 slides

NLP, ML, and AI — Basic Terminology

The foundational vocabulary you need: how Natural Language Processing, Machine Learning, and Artificial Intelligence relate to legal work.

Episode 212 slides

What Can AI Do in Legal?

A practical tour of what AI is doing right now in legal practice — contract review, legal research, and the realistic near-term implications for attorneys.

Episode 310 slides

Why AI? Why Now?

What changed, what made large language models possible, and why this moment matters for the legal profession.

Episode 46 slides

How Does AI Work? Part I — Pattern Recognition

How AI learns from data and makes predictions — the foundations of pattern recognition in plain English.

Episode 57 slides

How Does AI Work? Part II — Neural Networks

Neural networks explained: layers, training, and why the leap to large language models was so significant.

Episode 612 slides

OpenAI in a Nutshell

How OpenAI's approach to training on large datasets changed what's possible — and what that means for legal AI tools.

Episode 716 slides

NLP Deeper Dive

Tokenization, embeddings, and how AI systems convert text into something they can reason about.

Episode 812 slides

How to Train AI to Review Contracts

What labeled data is, why it's hard to create, and how it teaches AI to find specific clause types in real contracts.

Episode 914 slides

How to Measure AI Accuracy

Precision, recall, and F1 score in plain English — with contract review as the example. Know what you're trusting before you rely on it.

Episode 109 slides

AI Glossary (Episodes 1–9)

A consolidated reference covering every technical term from the first nine episodes. A handy cheat sheet to revisit.

Episode 1111 slides

Not All Things Require AI

A grounding perspective: when AI adds value versus when traditional approaches are faster, cheaper, and more reliable.

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