{"id":905433,"date":"2026-03-10T16:06:34","date_gmt":"2026-03-10T22:06:34","guid":{"rendered":"https:\/\/www.myconstructionexpert.com\/blog\/?p=905433"},"modified":"2026-03-10T16:06:38","modified_gmt":"2026-03-10T22:06:38","slug":"artificial-intelligence-definitive-glossary","status":"publish","type":"post","link":"https:\/\/www.myconstructionexpert.com\/blog\/artificial-intelligence-definitive-glossary\/","title":{"rendered":"The Definitive AI Glossary for Legal Professionals"},"content":{"rendered":"\n<p>Elizabeth Guthrie | <a href=\"https:\/\/www.nextpoint.com\/ediscovery-blog\/the-definitive-ai-glossary-for-legal-professionals\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Nextpoint<\/a><\/p>\n\n\n\n<p>The technical terminology surrounding AI is vast and complex. Whether you\u2019re just getting started or need a quick refresher, it\u2019s a good idea to familiarize yourself with some of these concepts before diving deeper into the world of AI. Take a look through this glossary and feel free to return to it as these terms crop up throughout your <a href=\"https:\/\/www.myconstructionexpert.com\/blog\/artificial-intelligence-what-is-it-insurance-wants-to-know\/\">AI<\/a> journey.<\/p>\n\n\n\n<h6 class=\"wp-block-heading\">TERMS INCLUDED<\/h6>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Artificial Intelligence (AI)<\/li>\n\n\n\n<li>AI Agent<\/li>\n\n\n\n<li>Chatbot<\/li>\n\n\n\n<li>ChatGPT<\/li>\n\n\n\n<li>Fine-Tuning<\/li>\n\n\n\n<li>Generative AI (GenAI)<\/li>\n\n\n\n<li>Hallucination<\/li>\n\n\n\n<li>Large Language Model (LLM)<\/li>\n\n\n\n<li>Machine Learning (ML)<\/li>\n\n\n\n<li>Natural Language Processing (NLP)<\/li>\n\n\n\n<li>Parameter<\/li>\n\n\n\n<li>Predictive Coding<\/li>\n\n\n\n<li>Prompt<\/li>\n\n\n\n<li>Recall and Precision<\/li>\n\n\n\n<li>Retrieval-Augmented Generation (RAG)<\/li>\n\n\n\n<li>Small Language Model (SLM)<\/li>\n\n\n\n<li>Technology Assisted Review (TAR)<\/li>\n\n\n\n<li>Token<\/li>\n<\/ul>\n\n\n\n<h5 class=\"wp-block-heading\">Artificial Intelligence (AI)<\/h5>\n\n\n\n<p>AI&nbsp;is a branch of computer science focused on creating systems that can perform tasks typically requiring&nbsp;human intelligence,&nbsp;such as reasoning, learning, problem-solving, perception, and understanding language.&nbsp;AI is everywhere,&nbsp;from the facial recognition you use to open your smartphone to the algorithmic recommendations you get on Netflix. While current conversations around AI have focused on generative AI systems, the term encompasses a much broader range of technologies that have been in use for decades.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">AI Agent<\/h5>\n\n\n\n<p>AI agents&nbsp;are systems that can act autonomously to achieve goals; they are designed to&nbsp;make independent decisions and take action without constant human guidance.&nbsp;Unlike traditional AI that responds to specific inputs, AI agents can plan sequences of actions, adapt to changing circumstances, and pursue objectives over time. This technology, known as&nbsp;<em>agentic AI<\/em>,&nbsp;is still in its early stages. Waymo\u2019s self-driving cars are one of the most advanced real-world deployments of agentic AI today, but this level of automation is not widespread in most industries. Still, AI agents have begun to pop up in legal technology, and this will likely increase as the technology continues to evolve.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Chatbot<\/h5>\n\n\n\n<p>A&nbsp;chatbot&nbsp;is a software application designed to&nbsp;simulate human-like conversation&nbsp;with users. Crude chatbots have existed since the 1960s, and they\u2019ve played a key (if often frustrating) role in online customer support for years. The release of ChatGPT by OpenAI in 2022 was a steep change in the capabilities of chatbots. Its combination of a&nbsp;large language model&nbsp;with training that emphasized successful conversations made it much more broadly applicable than any previous chatbot. New versions of ChatGPT, along with competitors such as DeepSeek, Microsoft\u2019s Copilot, Google\u2019s Gemini, and Anthropic\u2019s Claude, have been so successful that&nbsp;to many people, chatbots are simply synonymous with AI.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">ChatGPT<\/h5>\n\n\n\n<p>ChatGPT&nbsp;is a conversational AI model developed by OpenAI, based on the GPT (Generative Pre-trained Transformer) architecture. It is trained to generate&nbsp;human-like text responses in a conversational manner,&nbsp;useful for a variety of applications like answering questions, providing explanations, and engaging in dialogue. However, legal professionals should exercise caution when using open models like ChatGPT. You have little control over what happens to your data after it is processed by the chatbot, meaning sensitive information should not be shared. Additionally, ChatGPT can generate false or misleading responses that require careful verification.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Fine-Tuning<\/h5>\n\n\n\n<p>Most large language models (LLMs) are trained on masses of text that span vast ranges of topics \u2013 often whatever the trainer can get their hands on. A few are trained on text that is limited to a particular domain. However, a more common and generally less computationally demanding approach to&nbsp;tailoring LLMs to a particular subject is fine-tuning.&nbsp;This approach starts with a broad domain LLM and uses topic-specific or task-specific data to adjust some of its parameters. In the law, this might mean fine-tuning a generic LLM with&nbsp;documents from a particular law firm,&nbsp;or from a particular task such as&nbsp;contract clause extraction, risk analysis, or compliance monitoring.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Generative AI (GenAI)<\/h5>\n\n\n\n<p>As discussed in the foreword,&nbsp;generative AI&nbsp;refers to AI tools or components that&nbsp;create new content, such as text, images, music, or videos,&nbsp;based on learned patterns from existing data. After the launch of ChatGPT in 2022, generative AI became the&nbsp;center of the current AI revolution.&nbsp;GenAI holds huge potential for attorneys, since so much of legal work involves drafting contracts, briefs, and other types of documents.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Hallucination<\/h5>\n\n\n\n<p>Hallucinations&nbsp;refer to situations where a generative AI system produces&nbsp;outputs that are factually incorrect, inaccurate, logically incoherent, or completely fabricated.&nbsp;The most notorious examples are when large language models (LLMs) output \u201cfacts\u201d that sound plausible but are completely made up. Attorneys using GenAI to draft briefs have earned the ire of judges for fake case citations hallucinated by LLMs. Users of GenAI thus have a responsibility to&nbsp;verify information&nbsp;they get from AI tools, as these hallucinations can lead to dire mistakes if left unchecked.<\/p>\n\n\n\n<h6 class=\"wp-block-heading\">Hallucinations in the Courtroom<\/h6>\n\n\n\n<p>Surely lawyers everywhere learned a lesson from the first high-profile case where an attorney faced sanctions for citing fake cases hallucinated by ChatGPT. Right? Unfortunately, too many lawyers have ignored that lesson and echoed this sloppy use of AI. Legal researcher Damien Charlotin has\u00a0<a href=\"https:\/\/www.damiencharlotin.com\/hallucinations\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">compiled a database<\/a>\u00a0to track legal decisions related to AI hallucinations in the courts. This serves as a crucial reminder to always verify AI outputs before filing materials in a court of law. Explore the database at\u00a0<a href=\"https:\/\/www.damiencharlotin.com\/hallucinations\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">damiencharlotin.com\/hallucinations<\/a>.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Large Language Model (LLM)<\/h5>\n\n\n\n<p>A&nbsp;large language model (LLM)&nbsp;is a type of&nbsp;machine learning model,&nbsp;specifically a&nbsp;generative model,&nbsp;trained on massive data sets of text to&nbsp;replicate patterns in language.&nbsp;They can generate fluent language on almost any topic in response to natural language prompts, making them a key component of&nbsp;chatbots, document generation tools,&nbsp;and many other&nbsp;AI systems.&nbsp;The&nbsp;\u201clarge\u201d&nbsp;in LLM refers to the number of parameters in the model as well as the amount of training data used to produce them.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Machine Learning (ML)<\/h5>\n\n\n\n<p>Machine learning&nbsp;was traditionally a subfield of AI, but has arguably&nbsp;swallowed the whole field&nbsp;in recent years. ML algorithms allow computers to&nbsp;learn patterns from data and make decisions or predictions without being explicitly programmed.&nbsp;Modern computing systems have access to a vast amount of data about the world, and ML systems have the ability to&nbsp;adapt to changes&nbsp;in the world as reflected in that data. This has led to ML playing the dominant role in modern AI systems.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Natural Language Processing (NLP)<\/h5>\n\n\n\n<p>Natural language processing (NLP)&nbsp;is a branch of AI focused on the interaction between computers and human (natural) languages. It enables machines to&nbsp;read, understand, interpret, and generate human language,&nbsp;making it a key component of&nbsp;generative AI models.&nbsp;Research and development in NLP focuses on automating practical tasks involving language, such as document generation, question answering, text and multimedia search, language translation, and many others.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Parameter<\/h5>\n\n\n\n<p>Parameters&nbsp;refer to the&nbsp;internal variables or weights in an AI model that are learned during training.&nbsp;Think of parameters as the \u201cknobs and dials\u201d that an AI model tweaks while learning. These parameters help the model make predictions or generate outputs based on the input data it receives. Essentially, they are the&nbsp;settings that the AI adjusts during the learning process to minimize errors and improve its ability to perform tasks.&nbsp;The number of parameters in a model is often used as a measure of the model\u2019s complexity. For example, large models like GPT-3 and GPT-4 have billions or trillions of parameters, which enable them to perform complex tasks such as natural language generation.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Predictive Coding<\/h5>\n\n\n\n<p>Predictive coding&nbsp;is a process used in&nbsp;ediscovery&nbsp;where a machine learning algorithm is trained to&nbsp;identify relevant documents by learning from a set of human-reviewed examples.&nbsp;The algorithm then predicts the relevance of the remaining documents. Predictive coding can improve the efficiency and speed of document review and ensure that attorneys focus on the most important material.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Prompt<\/h5>\n\n\n\n<p>A&nbsp;prompt&nbsp;in AI is the&nbsp;input text or instruction given to an AI model to guide its response or behavior.&nbsp;It\u2019s essentially&nbsp;how you communicate with the AI system,&nbsp;telling it what you want it to do, what context to consider, or what kind of output you\u2019re looking for. The quality and specificity of the prompt directly influence the relevance and usefulness of the AI\u2019s response.&nbsp;<em>Prompt engineering&nbsp;<\/em>refers to the task of developing and refining prompts in order to generate the desired outputs.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Recall and Precision<\/h5>\n\n\n\n<p>In ediscovery and legal AI tools,\u00a0recall\u00a0and\u00a0precision\u00a0are two key metrics for\u00a0evaluating how well a system can identify relevant documents.\u00a0Recall represents the percentage of relevant documents retrieved from the body of data, while precision measures the number of documents labeled as relevant that were actually relevant. Put simply,\u00a0recall\u00a0answers the question,\u00a0\u201cDid you find everything?\u201d\u00a0while\u00a0precision\u00a0asks,\u00a0\u201cHow much extra junk did you pick up?\u201d\u00a0Balancing these two metrics helps ensure that no important document is overlooked while minimizing false positives.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Retrieval-Augmented Generation (RAG)<\/h5>\n\n\n\n<p>Retrieval-augmented generation (RAG)&nbsp;refers to generative AI techniques that leverage an&nbsp;LLM&nbsp;alongside a&nbsp;body of task-specific text.&nbsp;In response to a user prompt, a RAG system retrieves relevant information from the specified source to generate a response from the LLM. RAG approaches enable AI to work with&nbsp;specialized information&nbsp;and help&nbsp;reduce (but do not eliminate) hallucinations.&nbsp;RAG forms the foundation of Nextpoint\u2019s AI-powered transcript summary feature.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Small Language Model (SLM)<\/h5>\n\n\n\n<p>Small language models&nbsp;are designed for language tasks but trained on a&nbsp;smaller data set&nbsp;and&nbsp;optimized for specific applications,&nbsp;often with fewer parameters than large language models. While not as powerful as LLMs, small language models are still useful for specific legal applications. Because they are smaller, they may offer faster processing and can be more easily customized for niche legal tasks. However, it is important to note that there is&nbsp;no official technical distinction&nbsp;between small and large language models. Different software developers may have varying definitions for how many parameters constitute a \u201csmall\u201d vs. \u201clarge\u201d model.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Technology Assisted Review (TAR)<\/h5>\n\n\n\n<p>Technology Assisted Review (TAR),&nbsp;also known as&nbsp;Computer Assisted Review (CAR),&nbsp;refers to machine learning and AI techniques used to&nbsp;streamline the document review process in ediscovery.&nbsp;These technologies allow lawyers to&nbsp;review large data sets by prioritizing the most relevant documents,&nbsp;thus saving time and reducing human error. The earliest forms of TAR were centered on&nbsp;predictive coding,&nbsp;where attorneys review a small sample of documents and the AI learns from their decisions to classify the rest of the data automatically. In the past two years, TAR systems based on generative AI and prompting have emerged and show promise of producing more effective results with less manual review.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Token<\/h5>\n\n\n\n<p>Tokens&nbsp;are the&nbsp;basic units that AI language models use to process text.&nbsp;They can be&nbsp;words, parts of words, or even individual characters,&nbsp;depending on how the text is broken down. For example, the word \u201ctokenization\u201d might be split into tokens like \u201ctoken\u201d and \u201cization.\u201d Some AI tools employ&nbsp;token-based pricing,&nbsp;which functions similarly to the per-GB pricing models of traditional ediscovery software.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">What\u2019s Most Important for Legal Professionals?<\/h4>\n\n\n\n<p>Something as technically complex as AI inevitably brings plenty of complicated jargon to the table. You might feel overwhelmed reading through this list, and that\u2019s okay. You don\u2019t have to memorize each term \u2013 this glossary is a resource that you can continuously reference as you navigate the world of AI. For now, here are the themes that are most essential for legal professionals to understand:<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Generative AI and LLMs<\/h5>\n\n\n\n<p>As we mentioned,&nbsp;GenAI tools&nbsp;have inspired the recent wave of excitement around AI. Naturally, this means that a number of terms in this list tie back to generative AI in some way. You likely don\u2019t need to know the intricacies of natural language processing, but&nbsp;it\u2019s important to understand what GenAI is and how it differs from other types of AI.&nbsp;This can help you&nbsp;wade through the \u201cAI\u201d label&nbsp;that\u2019s slapped on so much new tech we see today. Additionally, you must understand the&nbsp;risks and limitations of GenAI,&nbsp;including hallucinations, to ensure it doesn\u2019t create issues for your practice.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Predictive Coding and TAR<\/h5>\n\n\n\n<p>These technologies&nbsp;introduced AI to the legal field in 2010&nbsp;and are still commonly used to streamline document review. However, these tools require far more manual input than the AI systems employed today. If you haven\u2019t already used TAR for document review, it likely won\u2019t be the first AI tool you jump to use in 2026.&nbsp;But understanding the history of AI in the legal field can help you understand where it\u2019s going as we face a digital revolution.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Fine-Tuning and RAG<\/h5>\n\n\n\n<p>These are two methodologies that enable developers to&nbsp;hone AI tools for specific tasks and subject matters like the law. Prompt engineering&nbsp;also plays a role in obtaining particular outputs from an AI system, which can even occur behind the scenes in an AI tool. These approaches can reduce \u2013 but not eliminate \u2013 hallucinations. AI products that employ these methods are&nbsp;more likely to be beneficial for the specific needs of the legal field.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\">Parameters and Tokens<\/h5>\n\n\n\n<p>Parameters and tokens&nbsp;are two terms that relate to the&nbsp;back-end structure&nbsp;of an AI model. As a legal professional, you\u2019ll rarely need to dive deep into technical concepts like these. However, parameters provide information about the&nbsp;size and complexity of a model,&nbsp;and&nbsp;pricing is often centered around tokens,&nbsp;so it\u2019s important to have some familiarity with these terms in case they crop up in your search for effective AI products.<\/p>\n\n\n\n<p>Bottom line? You don\u2019t need to become a data scientist.&nbsp;But knowing these basics will help you&nbsp;ask the right questions, evaluate new tools, and navigate technology with confidence&nbsp;as AI continues to evolve.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Get Your Copy of \u201cAI for the Rest of Us\u201d<\/h4>\n\n\n\n<p><em>AI for the Rest of Us<\/em>&nbsp;offers more than this AI glossary \u2014 it dives into legal applications of AI, covers the risks and ethics of using AI, and shares practical tips for implementing AI in your legal practice. Click the button below to learn more and buy your copy today.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p><strong>When one of your cases is in need of a construction expert, estimates, insurance appraisal or umpire services in defect or insurance disputes &#8211; please call Advise &amp; Consult, Inc. at 888.684.8305, or email <a href=\"mailto:experts@adviseandconsult.net\">experts@adviseandconsult.net<\/a>.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Elizabeth Guthrie | Nextpoint The technical terminology surrounding AI is vast and complex. Whether you\u2019re just getting started or need a quick refresher, it\u2019s a good idea to familiarize yourself with some of these concepts before diving deeper into the world of AI. Take a look through this glossary and feel free to return to&hellip; <a class=\"more-link\" href=\"https:\/\/www.myconstructionexpert.com\/blog\/artificial-intelligence-definitive-glossary\/\">Continue reading <span class=\"screen-reader-text\">The Definitive AI Glossary for Legal Professionals<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[172],"tags":[9895,3918,11331,219],"class_list":["post-905433","post","type-post","status-publish","format-standard","hentry","category-construction-law-2","tag-advise-consult","tag-ai","tag-artificial-intelligence","tag-construction-litigation","entry"],"jetpack_publicize_connections":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>The Definitive AI Glossary for Legal Professionals - Advise &amp; 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