Skip to content

Case & CodeA FIELD GUIDE TO AI

Inside AI Lessons Practice Articles Glossary

Inside AI Beginner AI in Practice Intermediate Build with AI Advanced

Reference

Glossary Prompt library Agent library Local AI guide Articles

Lessons by chapter

01AI essentials+

Chapter overview 1.1What actually happens when you ask AI a question?1.2Tokens, attention, and why wording matters 1.3What uploading a document does—and does not—establish 1.4Why a persuasive answer can still be wrong 1.5How models learn from examples 1.6Why the same question can produce different answers

02Using AI effectively+

Chapter overview 2.1Give AI a brief it can actually follow 2.2Keep evidence, instructions, and inference separate 2.3Improve a draft without changing its meaning 2.4Does AI learn from your corrections?2.5Build a reusable task pack

03AI in legal practice+

Chapter overview 3.1Build a chronology that survives checking 3.2Turn a transcript into useful examination preparation 3.3Research with AI without outsourcing the authority check 3.4Build a drafting workflow around the record 3.5Use AI to challenge an argument

04Tools and choosing between them+

Chapter overview 4.1Choose an AI tool around the work product 4.2Design a review table before generating it 4.3Turn dictation into a useful draft

05Agents, skills, and automation+

Chapter overview 5.1Prompts, skills, workflows, and agents 5.2Build your first agent around a document inventory 5.3When do multiple agents help?5.4Make recurring work resumable and reviewable 5.5Maintain project instructions without accumulating clutter 5.6Choose scripts, models, and human review deliberately

06Documents and knowledge systems+

Chapter overview 6.1Prepare documents before asking AI to analyze them 6.2Keyword search, semantic search, and reranking 6.3How document-grounded AI answers work 6.4Build a knowledge library you can actually reuse

07Local AI and building systems+

Chapter overview 7.1Understand the hardware a local model needs 7.2Run a local model and verify the actual setup 7.3Your first useful Python script: a document inventory 7.4APIs and JSON: connect a model to a small program 7.5Use a database to preserve facts and sources 7.6Prompting, retrieval, or fine-tuning?7.7Turn a prototype into a service you can maintain

08Reliability and oversight+

Chapter overview 8.1Test an AI workflow on work you can assess 8.2Decide whether an AI tip deserves your time 8.3Verify a work product before relying on it 8.4Recognize prompt injection in documents and tools 8.5Map where information goes before using a tool 8.6What courts expect when you use AI 8.7Reading AI-assisted material from the bench

09Local AI, in depth+

Chapter overview 9.1Why run AI on your own machine, and what you give up 9.2How a model actually runs on hardware 9.3Every part of a computer, through the lens of local AI 9.4Building a PC for local AI 9.5Devices other than a custom PC 9.6Engines and front-ends: the software layers 9.7Choosing a model: families, sizes, formats, and licences 9.8Setting it up: three tracks, easy and expert routes 9.9Proving it works: offline, measured, and checked against the formula 9.10Keeping it current: engines, models, and hardware

More

All chapters and paths Books, courses and sources About this project

Articles

For the curious legal mind.

Lessons Articles Glossary About

An independent learning library. Nothing here is legal advice.Revised October 2026 · see source notes

Search Case & Code

Lessons, terms, practical guides and sources ESC