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75 AI Terms Every Product Team Must Know (2025 Edition)

Build a shared AI language with clear definitions and meeting‐ready examples for product teams.

Author

Dr. Hernani Costa
November 18, 2025

As the founder of First AI Movers, I spend my days advising executives and product teams on navigating the AI revolution. I've sat in countless strategy meetings, workshops, and product reviews, and I've seen firsthand that the single biggest bottleneck to building great AI products isn't the technology—it's the lack of a common language.

When an engineer mentions "RAG," a designer talks about "AX," and a PM is worried about the "context window," the conversation fractures. This "terminology gap" slows development, creates deep misalignment, and ultimately results in weaker, less-focused products.

Why This Glossary Exists

AI is moving at an unprecedented speed, fundamentally transforming how we design, build, and experience software. To lead this transformation, your team needs a unified vocabulary. You cannot build a coherent AI strategy if your team can't communicate coherently.

What This Is (And Isn't)

This is the glossary I wish I had when I started my journey. It's not a dry, academic dictionary. It's a strategic toolkit—a 75-term glossary explicitly designed for product teams, leaders, and founders.

Each term includes two things:

  1. A concise, accessible definition.

  2. A product-oriented example to help you apply the concept in your very next meeting.

Who This Is For

This glossary is for the "First AI Movers"—the product managers, designers, engineers, marketers, and executives who are on the front lines of building the next generation of intelligent products. It’s for anyone who needs to bridge the gap between technical possibility and real-world business value.

My core belief is that clarity precedes mastery. This glossary is your first step. It's designed to be your team's single source of truth, an accessible guide to demystify the jargon and get everyone on the same page. This list will grow, just as our understanding of AI does.

Let's get fluent, together.

Note: if you find it useful and want to save it in your company wiki, let me know and I will share it in the format of your choice (Word, Markdown, JSON, etc.).

Quick pit stop: I run bespoke workshops, audits, and build sprints (automations & AI agents).
Start here → https://calendar.app.google/DjotURgHETCFHA7q6

The broad, interdisciplinary field of computer science focuses on creating intelligent systems capable of performing tasks that typically require human intelligence. This includes capabilities such as learning from data, reasoning, problem-solving, perception, and decision-making. These systems often rely on machine learning and deep learning to process information and improve performance over time.

Example: A navigation app using AI to predict traffic and reroute you in real time.

A sophisticated software entity or system that perceives its environment and can take autonomous actions on behalf of a user to achieve specific goals. AI agents often integrate with multiple systems, utilize reasoning capabilities, and can learn from interactions to improve their effectiveness. They represent a shift from passive tools to automated partners.

Example: A recruiting agent that screens resumes, schedules interviews, and drafts follow-up emails automatically.

An extension of User Experience (UX) specifically for the AI Age, focusing on human-AI interaction. The practice of designing agentic products that feel less like passive tools and more like collaborative, trusting relationships. Pioneered by the team at LCA, AX design prioritizes trust, explainability, and proactive assistance from the AI agent.

Example: Shortcut AI's agent will ask open questions to refine its task, then show reasoning as it generates output to build trust.

4. Alignment

The critical process and research field dedicated to ensuring an AI system's goals, behaviors, and outputs are consistent with human values, ethical principles, and intended objectives. AI alignment aims to prevent unintended, harmful, or unpredictable actions, especially as systems become more powerful, which is foundational for building safe and reliable AI.

Example: Adjusting a customer service AI to de-escalate angry users rather than respond aggressively.

5. Ambient AI

A paradigm of artificial intelligence that operates seamlessly and proactively in the background of a user's environment. Ambient AI, or ambient computing, surfaces value without requiring explicit prompts or direct interaction. It relies on sensors and context awareness to anticipate needs and automate tasks frictionlessly.

Example: A smart thermostat that adjusts temperature by learning your habits, without asking.

6. Anthropomorphization

The practice of assigning human-like traits, emotions, intentions, or characteristics to non-human entities, including AI systems. This can be done intentionally by product teams to build rapport, or it can happen unintentionally as users interact with conversational AI, impacting user trust and perception.

Example: Giving a customer-support bot a name, profile picture, and empathetic tone so users trust it more.

The use of AI and other technologies to fully perform tasks, workflows, or processes that would otherwise require human effort. This ranges from simple robotic process automation (RPA) to complex, AI-driven decision-making to increase efficiency, reduce errors, and scale operations.

Example: An e-commerce AI that writes, tags, and publishes product listings with no human edits.

A standardized test or set of tasks used to quantitatively evaluate and compare the performance, accuracy, and capabilities of different AI models. Benchmarks provide an objective measure for tracking progress and understanding a model's strengths and weaknesses in areas such as reasoning, language, and math.

Example: Using MMLU to compare reasoning ability between GPT-5 and Claude Haiku 4.5.

A reasoning and prompting technique where a model is prompted to outline its intermediate, step-by-step reasoning process before providing a final answer. This method improves accuracy on complex tasks, such as logic puzzles or math problems, and provides transparency into the model's problem-solving path.

Example: When asked for a cost calculation, the AI shows line-by-line math before the final result.

10. Cluster (GPU Cluster)

A group of high-performance computers, each equipped with multiple Graphics Processing Units (GPUs), that are linked together to work as a single, powerful system. GPU clusters are essential for computationally intensive tasks, such as training and running large-scale AI models, including LLMs.

Example: OpenAI uses GPU clusters with tens of thousands of NVIDIA chips to run GPT-5 at scale.

The emerging ability of an AI, particularly an AI agent, to directly control a computer's graphical user interface (GUI). This includes tasks like opening applications, moving the mouse, clicking buttons, or filling out forms, allowing the AI to operate software just as a human would.

Example: An AI travel assistant booking flights by controlling your browser in real time.

The set of information a model uses to understand and generate a relevant response. This can include the current prompt, conversation history, user metadata, or external documents provided via RAG. Providing clear, relevant context is crucial to AI performance and accuracy.

Example: A chatbot remembers you already asked about the refund policy, so it doesn’t repeat itself.

The maximum amount of information, measured in tokens, that an AI model can “see” and process at one time. This includes both the user's input and the model's generated output. A larger context window enables more extended conversations, analysis of entire documents, and more complex reasoning.

Example: A 200k token context window can store the entire contents of an employee handbook in a single session.

An AI product design pattern where the AI acts as an assistant to support a human user, rather than acting entirely autonomously. The copilot suggests, drafts, or refines content, but the human user remains in control, making final decisions, edits, and approvals.

Example: GitHub Copilot suggests code while the developer still decides what to use.

The standard billing units for using AI models via an API. A token is the basic unit of text a model processes (roughly ¾ of a word). Credits are the pricing units (e.g., dollars or points) that companies purchase and consume based on the number of input and output tokens used.

Example: Generating a 1,000-word report may consume ~1,300 tokens, billed as credits by the API.

A characteristic of traditional software systems is that they always produce the same output for a given input. This contrasts with probabilistic systems like LLMs. In AI products, deterministic logic is often used for guardrails, validation, and final actions (such as processing a payment).

Example: A password validator that always accepts the correct password and rejects the wrong one.

A core concept in AI where data (like words, images, or audio) is converted into a numeric vector representation. These vectors capture the "meaning" or semantic properties of the data, allowing AI models to find and compare items based on their conceptual similarity rather than just keywords.

Example: Using embeddings to let users search “How do I reset my password?” and retrieve the correct help doc even if the wording differs.

Short for "evaluations," these are structured tests and processes for measuring an AI model's performance, accuracy, quality, and safety. Evals can be automated (using benchmarks) or human-driven (using annotators) to ensure the model behaves as intended before and after deployment.

Example: Running evals to confirm an AI legal assistant consistently extracts “termination date” from contracts.

19. Escape Hatch

A critical UX mechanism that allows a user to easily exit an AI-driven process or automated workflow and return to a safe, familiar, or human-controlled state. Escape hatches are essential for building user trust and providing a fallback when the AI fails or misunderstands.

Example: A support chatbot offering a “Speak to a human” button when the AI struggles.

20. Evaluation Harness

An automated software framework or platform designed for systematically testing, benchmarking, and evaluating AI models. A harness allows product teams to run large sets of evals consistently across different models or versions to track regressions and improvements over time.

Example: Nightly automated evals to ensure a customer service AI stays accurate as new data arrives.

Also known as Explainable AI (XAI) - do not confuse it with xAI, this is the ability to interpret and understand how an AI system arrived at its output or decision. Explainability is crucial for debugging, auditing, ensuring fairness, and building user trust, especially in high-stakes domains such as medicine and finance.

Example: A credit-risk AI that shows the top three factors influencing its loan approval recommendation.

A prompt engineering technique for improving model performance by providing a small number (a "few") of labeled examples of the desired task directly within the prompt. This helps the model understand the target format or logic without requiring full fine-tuning.

Example: Feeding 5 example support tickets labeled “billing” or “technical” so the model classifies new tickets correctly.

The product-driven process of collecting explicit (e.g., thumbs up/down buttons) or implicit (e.g., user acceptance of a suggestion) feedback from users or systems. This data is then used to continuously evaluate, retrain, and improve an AI model's performance and alignment in production.

Example: Thumbs up/down buttons in ChatGPT that retrain future responses.

A class of artificial intelligence systems, including Large Language Models (LLMs), that can create new, original content (such as text, images, video, audio, or code) rather than just analyzing or acting on existing data. This content is generated based on patterns learned from vast training datasets.

Example: MidJourney generating original product mockups from a text description.

A cutting-edge concept where user interfaces (UI) are dynamically generated or modified by AI in real time, adapting to the user's specific context, query, or goals. This moves beyond fixed, pre-designed layouts to create personalized, one-of-a-kind interfaces.

Example: A product analytics tool that auto-builds the dashboard most relevant to your query.

Stands for “Generative Pre-trained Transformer,” this is the specific family of Large Language Models (LLMs) developed by OpenAI. The term "GPT" is also often used more generally to refer to any conversational AI chatbot powered by this type of technology.

Example: GPT-5 powers ChatGPT, capable of long-context reasoning and multimodal tasks.

The verified, correct, and high-quality data is used as the definitive benchmark for training or evaluating AI models. This "source of truth" is often created and curated by human experts and is used to measure the model's accuracy against a known-good standard.

Example: Labeling 1,000 customer emails with the “correct” categories before training an AI classifier.

The process of ensuring an AI model's outputs are linked to or "grounded in" verifiable, external facts or specific data sources. This is a key technique, often achieved with RAG, to combat hallucination and improve the factual accuracy and trustworthiness of the AI's answers.

Example: A medical AI answering based on Mayo Clinic research rather than its training corpus.

A set of rules, constraints, and filters designed to keep AI outputs safe, reliable, and within the intended scope of the product. Guardrails can be programmatic rules (e.g., block specific topics) or AI-based (e.g., a "safety layer" model) to prevent harmful, toxic, or off-brand responses.

Example: Blocking a health chatbot from giving unverified medical diagnoses.

The phenomenon where an AI, particularly an LLM, generates false, misleading, fabricated, or nonsensical information but presents it as factual. Hallucinations occur because models are probabilistic and optimized for coherence, not factual accuracy, making grounding techniques essential.

Example: A customer bot inventing a product feature that doesn’t exist.

A system design philosophy where humans remain involved in the AI process to review, approve, edit, or correct outputs. HITL is critical in high-stakes applications to ensure quality, handle exceptions, and provide a layer of human judgment that the AI lacks.

Example: An AI drafts credit approvals, but a loan officer must sign off.

The process of running a trained AI model to "infer" or generate predictions, classifications, or other outputs from new, live input data. This is the "live" phase of an AI model, as opposed to the "training" phase. Optimizing for inference speed (latency) is a key product concern.

Example: Using a trained recommendation model to suggest your next YouTube video.

33. Instruction-Following Model

A type of model, typically an LLM, that has been specifically fine-tuned to understand and follow human commands or instructions precisely. This is a shift from older models that were only trained to predict the next word, making them more valuable and controllable as product foundations.

Example: An Instruct GPT, trained to follow human commands, reliably summarizes text when asked.

A structured method of organizing and storing information where entities (like people, places, or concepts) are stored as nodes, and the relationships between them are stored as edges. Knowledge graphs provide rich, structured context that AI systems can use for more accurate reasoning and retrieval.

Example: A customer support AI using a knowledge graph to understand that “password reset,” “login issue,” and “account recovery” are all related concepts.

A massive AI model, based on the Transformer architecture, that has been trained on vast quantities of text data. LLMs have a deep understanding of language, grammar, and world knowledge, enabling them to understand, generate, summarize, and translate human-like language at a sophisticated level.

Example: Anthropic’s Claude 4 interpreting long policy documents and drafting recommendations.

The time delay between a user’s input (like sending a prompt) and the AI’s response. Low latency is critical for a good user experience, especially in conversational or real-time applications. High latency can make an AI product feel slow, broken, or unusable.

Example: A 1-second latency feels conversational, but a 10-second break breaks the flow.

37. Latency Budget

A product and engineering constraint that defines the maximum acceptable time a system or AI model can take to respond before the user experience is considered unacceptably poor. Setting a latency budget helps teams make trade-offs between model size, accuracy, and response speed.

Example: A shopping chatbot might have a 3-second latency budget; longer feels unusable.

A subfield of Artificial Intelligence (AI) that focuses on training algorithms (models) to learn patterns and make predictions from data, without being explicitly programmed with rules. Generative AI is a modern form of machine learning.

Example: Spotify’s ML models learning your listening habits to recommend playlists.

An AI agent's ability to retain, recall, and utilize information from past interactions, sessions, or provided documents. Memory can be short-term (within the context window) or long-term (stored in an external database), allowing for personalization and continuous, context-aware conversations.

Example: A shopping assistant remembers your clothing sizes over time.

Software that acts as an intermediary layer, connecting AI models to enterprise systems, databases, and APIs. AI middleware often handles tasks such as orchestration, data transformation, API management, and enforcement of compliance and security rules, making it easier to integrate AI into existing workflows.

Example: Middleware ensuring an AI copilot pulls only the latest HR policies when answering employee questions.

Also known as Small Language Models (SLMs), these are smaller, highly optimized AI models designed for speed, efficiency, and lower operational costs. They are often used for specific, less complex tasks (such as classification or summarization) or for running "on-device" (e.g., on a smartphone).

Example: GPT-4o mini powering lightweight chatbots inside customer apps.

The core "brain" of an AI system. It is a complex algorithm, like a neural network, that has been "trained" on a massive dataset to recognize patterns. Once trained, the model is the file that transforms new input data (e.g., a prompt) into useful output (e.g., a prediction or generated text).

Example: A spam detection model that flags unwanted emails.

An emerging framework or standard for securely and efficiently connecting AI models to private, organizational data sources and workflows. MCP is a "universal adapter" that enables any model to access a company's tools and data securely.

Example: Using MCP so an internal AI assistant can answer only from a company’s Confluence pages.

A sophisticated AI system composed of multiple, specialized AI agents that work together to achieve a complex goal. Each agent is assigned a specific role or sub-task and communicates with the other agents to coordinate the workflow, much like a human team.

Example: A “writer” agent drafting a blog, a “fact-checker” agent verifying claims, and an “editor” agent refining tone.

An AI model's ability to process, understand, and generate information across multiple types (or "modes") of data, such as text, images, audio, and video. A multimodal AI can, for instance, look at a picture, understand its content, and generate a text description about it.

Example: An AI that interprets a product photo and generates both a written description and a spoken ad script.

The everyday spoken or written language used by humans to communicate, such as English, Spanish, or Japanese. AI models are trained to understand the complex rules, grammar, and nuances of natural language to enable human-computer interaction.

Example: Asking “What’s the weather tomorrow?” is a natural language query that an AI parses and answers.

A user interface (UI) where people interact with software using conversational, natural language (either typed or spoken) instead of traditional GUIs (buttons and menus) or command-line instructions. Chatbots are the most common form of NLUI.

Example: Typing “Book me a flight to New York next Tuesday” directly into a travel app’s chat box.

The broad field of AI focuses on enabling machines to understand, interpret, analyze, and generate human language. NLP encompasses tasks like sentiment analysis, text classification, and machine translation, and is the foundational technology behind Large Language Models.

Example: Gmail’s “Smart Compose” uses NLP to finish your sentences as you type.

The practice of monitoring, measuring, and debugging AI systems while they are running in production. AI observability involves tracking metrics like cost, latency, hallucination rates, and response accuracy to understand model behavior and diagnose issues quickly.

Example: Tracking hallucination rates or measuring response accuracy for a deployed AI chatbot.

A prompt engineering technique, similar to few-shot learning, where a model is given only a single example of a task in the prompt. This single example helps the model understand the desired output format or logic, allowing it to generalize to new cases.

Example: Showing one example of a custom invoice format so the model processes new invoices correctly.

The coordination layer in an AI system that manages and routes complex tasks across different models, agents, tools, and databases. The orchestrator acts as the "general contractor," deciding which tool to call (e.g., running code, searching the web, or calling an API) to fulfill the user's request.

Example: LangChain orchestrating whether an AI should call search, summarization, or code execution tools.

52. Overfitting

A common failure mode in machine learning is where a model learns its training data too well, including its noise and idiosyncrasies. An overfitted model performs exceptionally well on the data it was trained on, but fails to generalize and performs poorly on new, unseen data.

Example: A churn prediction model that works perfectly on historical customers but fails on new ones.

53. Personification

The intentional product design choice of giving an AI agent a defined identity, name, role, or "voice." This is a form of deliberate anthropomorphization used to shape how users interact with the AI, build trust, and align the agent's tone with the brand.

Example: Naming your finance agent “Lexi” to feel like a trusted advisor.

The nature of Generative AI systems is that they produce outputs based on statistical probabilities rather than fixed rules. This means that even with the same input prompt, a model may deliver slightly different answers each time. This is the opposite of a deterministic system.

Example: Asking a chatbot the same question twice may yield slightly different answers.

The input instruction, query, or command provided by a user to an AI model to elicit a response. A prompt can be simple (a single question) or complex (containing instructions, context, and examples) and is the primary way users interact with LLMs.

Example: “Write a one-paragraph summary of this meeting transcript.”

The user interface (UI) element, typically a text box, where users enter their prompts to interact with an AI. The design of the prompt bar and its surrounding elements (e.g., file upload buttons, suggestion chips) is a key part of the AI product's user experience.

Example: The ChatGPT text box or Figma’s AI assistant input field.

The practice of designing, refining, and optimizing effective prompts to guide an AI model's behavior and improve the quality, accuracy, and relevance of its output. This is a critical skill for building reliable AI-powered features and products.

Example: Reframing “Summarize” as “Summarize in 3 concise bullets for executives.”

A powerful technique where an LLM first retrieves relevant information from an external, up-to-date knowledge base (like a vector database) before generating an answer. RAG "grounds" the model in facts, reducing hallucinations and allowing it to answer questions about private or recent data.

Example: A support bot pulling answers directly from your knowledge base.

An AI model, or a specific version of a model, that is optimized for multi-step, logic-heavy, and complex reasoning tasks rather than just simple conversation or creative generation. These models are trained to "think" more deeply before providing an answer.

Example: A reasoning model used in legal tech to analyze arguments across hundreds of case files.

A type of machine learning where an AI model is trained by trial and error, receiving "rewards" for desirable actions and "penalties" for poor ones. This feedback loop teaches the AI to develop a strategy that maximizes its cumulative reward over time.

Example: A recommendation system learning to maximize click-through rate.

61. Safety Layer

A protective filter, often a separate, specialized AI model, that sits between the main AI model and the user. This layer's sole job is to check the AI-generated output for harmful, toxic, biased, or unsafe content and block it before it reaches the user.

Example: A moderation system blocking unsafe chatbot responses before they reach users.

62. Self-Play

An advanced AI training technique, often used in reinforcement learning, where the system learns and improves by competing against itself. The AI generates its own training data by playing millions of games, constantly refining its strategy to beat its previous versions.

Example: AlphaZero mastering chess and Go by generating its own training data through play.

63. Swarm

A type of multi-agent architecture where a group of AI agents work on different sub-tasks of a larger goal in a loosely coordinated, often parallel, fashion. The agents may then consolidate their findings, "vote" on the best approach, or pass their work to a final "editor" agent.

Example: A swarm of agents, each researching different competitors, then consolidating results.

64. Synthetic Data

Data that is generated artificially by AI, rather than being collected from real-world events or users. Synthetic data is used to augment or create training datasets, especially in privacy-sensitive domains (like healthcare) or for rare edge cases where real data is scarce.

Example: Creating synthetic patient data to train a healthcare model without exposing real records.

AI-generated user profiles or "personas" that are created to simulate real users. These personas can be used for product testing, prototyping, or simulating how different user segments might interact with an AI-powered feature before it is released to the public.

Example: Creating 50 synthetic personas (e.g., “busy parent,” “budget traveler”) and running them through an AI-powered prototype.

66. Toolchain

The set of external tools, services, APIs, or code libraries that an AI agent is given access to and can use to complete tasks. A toolchain might include a web search API, a calculator, a function to query a database, or a Stripe integration for payments.

Example: An agent that calls Stripe for payments, Slack for messaging, and Google Maps for routing.

67. Transfer Learning

A machine learning technique in which a model pre-trained on a massive, general dataset (like all of Wikipedia) is reused and fine-tuned on a new, smaller, and more specific dataset. This "transfers" the model's general knowledge to a specialized task, saving time and data.

Example: Fine-tuning a vision model trained on ImageNet to detect dental X-rays.

A specific, modern neural network architecture that is the foundational technology behind most Generative AI, including LLMs. Its key innovation is the "attention mechanism," which allows the model to weigh the importance and relationships between different tokens (words) in a sequence, enabling it to understand long-range context and scale effectively.

Example: GPT, Claude, Gemini, and LLaMA all use transformer architectures.

69. Trust Boundary

The critical point in a product workflow where the AI's probabilistic, generative outputs are handed off to a deterministic, rule-based system for execution. This boundary is essential for safety, ensuring that an AI's suggestion (e.g., "approve payment") is checked by a system that validates (e.g., "confirm funds exist") before acting.

Example: An AI recommends treatment options, but only a deterministic checklist approves prescriptions.

The process of specializing a general, pre-trained base model (like GPT-4) by training it further on a smaller, domain-specific dataset. Fine-tuning adapts the model to a specific task, infuses it with expert knowledge, or aligns it with a particular brand tone.

Example: Fine-tuning GPT with customer support transcripts to reflect brand tone.

A specific type of database optimized explicitly for storing and querying embeddings (numeric vectors). Vector databases enable "semantic search," allowing an application to find the most conceptually similar items to a query at massive scale, making them a core component of RAG systems.

Example: Using Pinecone or Weaviate to let users search company policies by meaning instead of keywords.

An emerging, conversational method for building software in which product specification, UI design, and code generation occur simultaneously in a natural language chat. A product team "vibe codes" by describing their goal to an AI, which iteratively generates and refines the working application.

Example: A team “vibe codes” a new onboarding flow by chatting with an AI that outputs working code and UI instantly.

The practice of developing and executing a complete marketing strategy conversationally with an AI agent. The AI handles planning, audience segmentation, asset creation (copy and images), and media deployment by integrating with marketing automation tools. Pioneered by the team at Boring Marketing.

Example: A CMO “vibe markets” a new campaign - the AI drafts strategy, designs assets, and pushes them live via ad integrations.

An AI, often an LLM, that communicates conversationally through speech rather than just text. This involves three technologies: speech-to-text (transcribing the user), the AI model (thinking), and text-to-speech (generating a spoken response). Modern voice agents can operate in real-time and are often interruptible.

Example: ChatGPT’s voice mode acts as a live conversational tutor.

A powerful capability of modern LLMs where the model can successfully perform a task without seeing any examples of that task in its prompt. The model relies on its vast pre-training to understand the instruction and generalize its knowledge to the new, unseen task.

Example: Asking a model to summarize legal contracts without training it specifically on legal data.

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