Glossary
Definitions of AI and legal technology terms used throughout LegalRealist. Terms are linked from posts at first mention. The glossary updates automatically as new posts introduce new concepts.
A
Agentic AI (AI agents, agentic)
AI systems that go beyond single-prompt responses to autonomously plan, execute, and iterate on multi-step tasks — researching across sources, drafting documents, running analyses, and refining outputs without requiring human intervention at each step. Distinguished from basic chatbot interactions by the ability to use tools, manage context across steps, and self-correct.
Anomaly Detection
A set of statistical and machine learning techniques used to identify data points, events, or patterns that deviate significantly from expected behavior. In government investigations and compliance, anomaly detection can flag suspicious transactions, unusual billing patterns, or outlier behaviors across large datasets — finding the needles that human reviewers would miss.
Application Layer
The custom software a vendor builds on top of a foundation model — including prompts, retrieval pipelines, fine-tuning, user interface, and workflow logic. Most “proprietary AI” in legal tech is application-layer work; the foundation model itself is licensed from OpenAI, Anthropic, Google, or another lab.
First introduced in The Foundation.
C
Chunking
The process of splitting documents into smaller segments (chunks) for storage in a vector database. When a user queries the system, the most relevant chunks are retrieved and fed to the model as context. Chunk boundaries matter enormously: too small and the model loses surrounding context; too large and irrelevant content dilutes the signal. In legal documents, poor chunking can sever a clause from its definitions section or a finding from its evidentiary basis.
Context Rot
The phenomenon where language models perform measurably worse as the amount of input text grows, even when the task itself doesn’t get harder. A model that correctly identifies a clause in a 10-page contract may miss the same clause in a 200-page filing — not because the task changed, but because more context dilutes the model’s attention. Directly relevant to evaluating vendor claims about large context windows.
Context Window
The maximum number of tokens a model can process in a single prompt — its working memory. A 200K-token window holds a lengthy contract and exhibits; 1M–2M token windows can ingest entire deal rooms. If a document exceeds the window, the model can’t reference earlier content when analyzing later content.
First introduced in The Foundation.
E
Embeddings (vector embeddings, vector database)
Numerical vector representations of text produced by a neural network, where semantically similar texts are mapped to nearby points in a high-dimensional space. Embeddings power semantic search in RAG systems: instead of matching keywords, the system finds text whose meaning is closest to the query. The quality of embeddings determines whether a retrieval system finds the right passages or misses them.
F
Fine-Tuning
The process of further training a pre-trained foundation model on a specific dataset to specialize it for a task or domain. Distinct from prompt engineering (which changes inputs) and retrieval (which changes what context the model sees). Most legal AI tools rely more heavily on retrieval than fine-tuning because legal content changes faster than fine-tuning cycles.
First introduced in The Foundation.
Foundation Model
A large, general-purpose model trained on broad data that can be adapted to many downstream tasks through fine-tuning, prompting, or retrieval. Examples: OpenAI’s GPT family, Anthropic’s Claude family, Google’s Gemini family, Meta’s Llama family.
First introduced in The Foundation.
Frontier Lab
A company building the most capable foundation models. As of 2026, the major frontier labs are OpenAI, Anthropic, Google DeepMind, Meta, xAI, and DeepSeek. Training a frontier model costs $100M+ and requires thousands of specialized processors.
First introduced in The Foundation.
H
Hallucination
When a language model generates content that is fluent and plausible but factually wrong — citing a nonexistent case, misstating a holding, fabricating a statute. Not a bug; a structural feature of probabilistic text generation. Can be reduced through retrieval and verification but not eliminated.
First introduced in The Fundamental Limits.
K
Knowledge Graph (GraphRAG)
A structured database that represents entities (people, organizations, cases, statutes, concepts) as nodes and their relationships as edges, enabling queries that traverse connections rather than just match keywords. In legal AI, knowledge graphs power citation networks (like Shepard’s and KeyCite), connect related cases and statutes, and help retrieval systems understand that a party in one filing is the same entity referenced differently in another.
L
LLM (Large Language Model)
An AI system trained on large volumes of text to predict and generate language. Modern LLMs are built on transformer architectures and trained on hundreds of billions to trillions of words. The technology underneath nearly every legal AI tool.
First introduced in The Foundation.
Lost in the Middle (U-shaped attention bias)
A well-documented bias in transformer-based language models where information placed in the middle of a long input receives less attention than information at the beginning or end. Research shows a U-shaped attention curve: models are most accurate when relevant information appears in the first or last positions. For lawyers submitting large document sets to AI tools, this means document ordering can affect output quality.
O
Open-Weight Model
A foundation model whose parameters (weights) are publicly released, allowing self-hosting and fine-tuning. Examples: Meta’s Llama, DeepSeek’s R1, Mistral’s models. Distinct from “open-source” in the strict sense, which would require training data and code to also be released. Allows firms to process documents without sending them to a third-party API.
First introduced in The Foundation.
P
Prompt Engineering
The practice of designing inputs to a language model to produce useful outputs. Includes structuring instructions, providing examples, and chaining multiple prompts together. The least technical layer of LLM application development; often the highest-leverage one.
R
RAG (Retrieval-Augmented Generation)
A technique where a system first retrieves relevant documents from a verified database, then provides them to the language model as context for generation. Used by virtually every serious legal AI tool to ground outputs in real sources rather than relying on the model’s training data alone. Reduces hallucination but does not eliminate it.
First introduced in The Fundamental Limits.
T
Token
A subword unit that language models process — roughly equal to four English characters or three-quarters of a word. APIs charge separately for input tokens (what you send) and output tokens (what the model generates), with output typically costing 3–10x more.
First introduced in The Foundation.
Transformer
The neural network architecture introduced in the 2017 Google paper “Attention Is All You Need,” underlying nearly every modern language model. Built around “self-attention,” which lets the model weigh how every word in a passage relates to every other word, regardless of distance.
First introduced in The Foundation.
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