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 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.
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 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.
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 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 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 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.
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.
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.
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.
A complete 2026 explainer of legal AI: what it is, how it works (NLP, large language models, and RAG), the main use cases, the types of tools, the benefits, the limits and risks, and how to choose a platform you can verify.
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