Agentic AI
AI systems capable of carrying out multi-step tasks with some autonomy, such as researching, drafting, and revising a document in sequence without step-by-step human instruction.
AI Adoption Curve
A way of describing where an organisation or individual sits in the process of trying, trusting, and embedding AI tools into everyday practice, from early experimentation to routine use.
AI Governance
An organisation's internal policies, approval processes, and oversight structures for how AI tools may be used, by whom, and with what safeguards.
AI Literacy
A practical understanding of how AI tools work, their limitations, and how to use them appropriately.
AI Slop
Low-quality, generic content produced by AI with little human oversight, often mass-produced and lacking accuracy, originality, or editorial value.
AI Washing
Marketing a product or service as AI-powered when its actual use of AI is minimal or exaggerated.
Application Programming Interface (API)
A defined way for one piece of software to send requests to and receive responses from another, commonly how organisations connect their own systems to an AI provider's model.
Attention Mechanism
A technique that allows an AI model to weigh which words in a passage of text are most relevant to each other, rather than processing text strictly word by word. The advance that enabled modern large language models.
Chat Model
An AI model designed to respond to a prompt in a single, near-instantaneous pass, as distinct from a reasoning model that works through intermediate steps.
Closed Model
An AI model whose internal workings are not publicly available, typically accessed only through a company's own paid API or product.
Confidentiality Risk
The risk that confidential information could be exposed or retained when using AI tools, particularly where data is sent to a third-party provider's servers.
Context Window
The amount of text an AI model can process at once. A larger context window allows longer documents or more conversation history to be considered in a single request.
Contract Review Tool
Software that uses AI to read contracts and flag clauses, risks, or deviations from a firm's standard positions or playbooks, speeding up due diligence and negotiation prep.
Copilot
An AI assistant embedded within existing software, such as a word processor or practice management system, designed to help with tasks like drafting or summarising within that tool.
Custom Instructions
Standing preferences a user sets within an AI tool, such as preferred tone or format, which are applied automatically across chat sessions without being repeated in every prompt.
Due Diligence Automation
The use of AI tools to speed up reviewing large volumes of documents, such as identifying key clauses, risks, or missing information.
E-Discovery
The process of identifying, collecting, and reviewing electronic documents for use as evidence in litigation, increasingly assisted by AI-based document review tools.
Fine-Tuning
Further training an existing AI model on a narrower, specific dataset so it performs better on a particular task or domain.
Frontier Model
One of the most advanced AI models available at a given time, developed by a small number of large AI companies. Most AI-branded products are built on top of a frontier model rather than a model built from scratch.
Generative AI
AI systems that create new content, such as text, images, or code, rather than simply analysing or classifying existing material. The category that includes tools like ChatGPT and Claude.
Guardrails
Rules, checks, or technical controls built around an AI system to limit errors, prevent misuse, or keep output within defined boundaries.
Hallucination
When an AI system generates information, such as a fact, quote, or citation, that sounds plausible but is factually incorrect or entirely invented.
Human In The Loop
A workflow design where a person reviews, checks, or approves AI-generated output before it is used or relied upon, rather than the AI acting unsupervised.
Jailbreaking
Deliberately phrasing or structuring a prompt to get an AI model to bypass its built-in restrictions and produce output it would normally refuse to generate.
Junior Lawyer Training Gap
A concern that AI tools taking over routine drafting and research tasks may remove the hands-on experience junior lawyers traditionally used to develop core skills.
Knowledge Management
The organised capture, storage, and reuse of an organisation's know-how and past work product, supported by AI tools that surface relevant material automatically.
Large Language Model (LLM)
An AI model trained on very large volumes of text to predict and generate language. The technology underlying tools such as ChatGPT, Claude, and Gemini.
Legal Research Tool
AI-powered software that searches case law, legislation, and secondary sources to answer legal questions or find relevant authorities, often producing draft summaries with citations.
Legal Tech Stack
The combination of software tools, including AI applications, that a firm or legal team uses together to manage its work, from document management to research and drafting.
Model Context Protocol (MCP)
A standard that allows AI models to connect to external tools and data sources, such as a document management system, in a consistent way.
Model Weights
The internal numerical parameters an AI model learns during training, which determine how it processes input and generates output.
Open-Source Model
An AI model whose underlying code or weights are published for anyone to use, inspect, or modify, as distinct from closed, proprietary models accessed only via a paid service.
Output Verification
The step of checking AI-generated content against reliable sources before relying on or submitting it.
Persistent Memory
A setting in some AI tools that allows the model to retain and reuse information from earlier chat sessions, rather than treating each session as fully separate.
Privilege Risk
The risk that using AI tools in a particular way could affect a claim of legal professional privilege over a document or communication.
Prompt Engineering
The practice of crafting instructions given to an AI model to produce more accurate, relevant, or consistently formatted output.
Reasoning Model
An AI model that breaks a prompt and its response into multiple intermediate steps before producing a final answer, rather than generating a response in one pass.
Retrieval-Augmented Generation (RAG)
A method where an AI model retrieves relevant documents from a database before generating an answer, so responses are grounded in specific source material rather than general training data.
Synthetic Data
Artificially generated data used to train or test AI systems, sometimes used to avoid exposing real personal or confidential information during development.
System Prompt
A set of instructions built into an AI tool by its provider, sitting between the user's prompt and the model, that shapes or restricts how the model can respond before the output reaches the user.
Technology-Assisted Review (TAR)
A document review method where AI ranks or classifies documents by likely relevance, based on decisions made by human reviewers on a sample set. Also known as predictive coding.
Token
A unit of text, roughly a word or part of a word, used by AI models to process language and by AI providers to measure usage and cost.
Training Data
The text, images, or other material used to teach an AI model to generate language or make predictions. Its quality and scope shape what the model can and cannot do well.
Unsupervised Learning
A training process where an AI model identifies patterns and relationships in data on its own, without being given labelled examples of correct answers.
Vector Database
A database that stores text or other data as numerical representations of meaning and relationships, allowing an AI system to compare and retrieve related content.
Vibe Coding
Writing software by describing what you want in plain language and letting an AI tool generate the code, with little or no manual coding by the person directing it.
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