A standardized documentation artifact describing an AI model's intended use, performance characteristics, limitations, and training data — essential for legal AI vendor due diligence.
Last reviewed: 2026/05/18
An AI-driven multi-step legal process — such as intake to routing to drafting — that executes autonomously across defined stages without per-step human prompting.
Tech / ModelA quantitative measure of how often an AI system produces correct outputs on a defined test set — critical for evaluating legal AI tools where errors carry professional responsibility risk.
Tech / ModelA standardized evaluation measuring an AI system's accuracy, reliability, or performance on defined legal tasks — used to compare tools and validate fitness for professional use.
Tech / ModelAn AI architecture combining a language model with a retrieval system that fetches relevant documents at query time, grounding responses in authoritative source material to reduce hallucination.
The most expensive legal AI in the market — Am Law 100 firms only.
Thomson Reuters' GPT-backed legal research and drafting with Westlaw integration (relaunched as CoCounsel Legal, 2025).
Purpose-built US legal AI covering research, drafting, and compliance.
Move from this definition to role-based legal AI shortlists and the selection criteria that matter for each type of legal team.
Am Law 200 and global firm workflows: accuracy at scale, security compliance, and matter-level auditability.
60 legal AI tools vetted for the solo lawyer: tight budget, no IT team, billable-hours pressure.
Last reviewed: 2026/05/18. Definitions are written by the LawyerAI Editorial team. Commercial relationships are disclosed and do not determine editorial scores or conclusions. See our Sponsorship & Affiliate Disclosure.
A model card is a short document published alongside a machine learning model that discloses key facts about how it was built and how it should — and should not — be used. Originating in academic research, model cards are now increasingly expected by enterprise buyers and regulators. For legal AI products, a model card should address intended legal tasks, languages and jurisdictions covered, benchmark performance on legal datasets, known failure modes, and data provenance.
Lawyers and legal operations teams conducting AI vendor due diligence should treat the absence of a model card as a red flag. Without it, there is no structured way to evaluate whether a model is fit for a specific legal purpose — such as contract review under New York law versus EU regulatory compliance analysis. Model cards also serve as a starting point for professional responsibility risk assessments, helping firms document the basis for their competent use of AI tools.