Plain-English explainers for the core concepts behind legal AI, from retrieval-augmented generation and legal LLMs to TAR and AI contract review, each defined clearly and without jargon.
13 articles
A plain-English explainer of machine learning versus generative AI in legal work: what each is, how they nest, the practical difference between classifying and creating, where each is used - from technology-assisted review to drafting - and why the distinction changes how you verify.
You do not need to code to evaluate legal AI: you need to understand three pieces of plumbing: retrieval (finding real sources), grounding (drafting only from them), and citations (proving it). A lawyer's guide to the machinery, minus the jargon.
A balanced, dimension-by-dimension comparison of AI and traditional Boolean legal research (query style, recall, speed, citations, coverage, cost, and risk) with a clear answer: use both, and verify everything.
A plain-English explainer on AI legal research: what it is, how semantic search, RAG, and citations make it work, what it can and cannot do, and how to use it without inheriting the risk of hallucinated authority.
A plain-English explainer of large language models for law: how LLMs predict text, why they hallucinate, the difference between general-purpose and legal-tuned models, and how prompting, fine-tuning, and retrieval-augmented generation ground them in cited sources.
A plain-English explainer of retrieval-augmented generation (RAG) for legal AI: the pipeline from chunking to cited generation, why grounding matters, how RAG compares with fine-tuning and prompting, and why it reduces but does not eliminate hallucination.
What an AI paralegal is and what it does: reviewing and summarising documents, legal research, extracting key terms and dates, building chronologies, and drafting support - plus the hard limits that keep a human paralegal firmly in charge.
Legal AI explained in plain English: research, contract review, drafting and translation, with jurisdiction examples and a practical checklist for choosing a tool.
A clear explainer of TAR and predictive coding: how machine learning ranks documents by relevance in e-discovery, seed sets and continuous active learning, recall and precision, how courts treat it since Da Silva Moore, and why it differs from generative AI.
A clear explanation of legal document automation: how classic template logic with variables and conditional clauses differs from AI-assisted generative drafting, the benefits of speed and consistency, and the limits that still require a lawyer to settle and own every document.
A plain-English glossary of the key legal AI terms (artificial intelligence, machine learning, LLMs, RAG, hallucination, TAR, citation grounding, human-in-the-loop, and more) each defined in a sentence or two, grouped for easy scanning.
A clear explainer of AI contract review: how AI extracts and classifies clauses, flags risks, and compares contracts against a playbook across many documents, what it reliably catches, what it misses, and why a lawyer still owns the final review.
A practical guide to prompt engineering for legal work: give the model a role and context, be specific, set constraints and format, use examples, demand citations, scope by jurisdiction, and iterate - while remembering that prompts never replace verification.
Get started
Run your first review free in minutes, or book a demo and see Judicio on your own matters.
Free trial · No credit card required