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Case & CodeA FIELD GUIDE TO AI

Understand AI Lessons Practice Articles Glossary

Guided learning paths

Understand AI Beginner Use AI in Your Work Intermediate Build AI Systems Advanced

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Glossary Prompt library Skills library Agent library Local AI guide Articles

Chapters by subject

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 1.6A short history of language models 1.7Why the same question can produce different answers

02Using AI effectively+

Chapter overview 2.1What a prompt is, including the part you never see 2.2Give AI a brief it can actually follow 2.3Keep evidence, instructions, and inference separate 2.4Package source material so the model can use it 2.5Ask for the working: chain of thought and reasoning models 2.6Improve a draft without changing its meaning 2.7Does AI learn from your corrections?2.8Build a reusable task pack

03Legal Workflows+

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 5.2Give the model the facts, or give it tools 5.3Build your first agent around a document inventory 5.4When do multiple agents help?5.5Make recurring work resumable and reviewable 5.6Maintain project instructions without accumulating clutter 5.7Choose 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, jailbreaks, and leaks 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

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

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