Agents
Software entities that can plan, act, and adapt on your behalf. Example: A travel-booking agent that searches flights, compares hotels, and revises the itinerary after you say, “Make it cheaper.”Algorithm
A step-by-step set of rules a computer follows to solve a problem. Example: The recipe a search engine uses to rank web pages.Anthropic
An AI research company best known for the Claude family of large language models. It focuses on building AI that is “helpful, honest, and harmless.”Artificial General Intelligence (AGI)
A still-theoretical AI that can match or exceed human cognitive abilities across any task, not just narrow ones like translation or chess.Artificial Intelligence (AI)
The broad field of building machines that perform tasks requiring human-like intelligence—from playing Go to recognizing faces.Artificial Super Intelligence (ASI)
A hypothetical future AI whose intellect vastly surpasses the best human minds in every field, from science to creativity.Chain-of-Thought
The intermediate reasoning steps a model generates before its final answer. Example: Showing its step-by-step math when solving “12 x 17 = ?”ChatGPT
OpenAI’s conversational interface built on GPT models. It answers questions, writes text, and reasons through problems in natural language.Chips
Specialized hardware (GPUs, TPUs, NPUs) that accelerates AI training and inference by handling many math operations in parallel.Claude
Anthropic’s flagship LLM, designed to be helpful and less likely to produce unsafe content. Versions include Claude 2 and Claude 3.Context Window
The maximum amount of text (tokens) a model can “see” at once. A bigger window lets the model remember longer prompts or documents.Deep Learning
A subset of ML that uses multi-layered neural networks to automatically learn patterns from large amounts of data—key to today’s GenAI.DeepSeek
An open-source project offering competitive LLMs (e.g., DeepSeek-Math, DeepSeek-Coder) that specialize in technical reasoning and code.Embedding
A dense numerical vector that captures the meaning of text, images, or other data in a way computers can compare. Example: Sentences with similar meanings have vectors that are close together in space.Few-shot Learning
Teaching a model a new task by showing it just a handful of examples—often 1–10—inside the prompt.Fine-tuning
Taking a pre-trained model and training it a bit more on domain-specific data so it speaks your brand’s voice or follows special rules.Foundation Models
Massive, general-purpose models (text, vision, or multimodal) trained on broad data and later adapted to many downstream tasks.Gemini
Google DeepMind’s flagship family of multimodal foundation models (Gemini 1.0 Ultra, Pro, Nano) built to handle text, images, audio, and more.Generative AI (GenAI)
AI systems that create new content—text, images, code, music—rather than just analyze existing data. ChatGPT and DALL·E are GenAI.Google DeepMind
Google’s research unit (formerly DeepMind) behind breakthrough AI systems like AlphaGo, AlphaFold, and Gemini.Grounding
Linking a model’s output to real-world data or verified sources to ensure it’s factual and relevant. Example: Citing a live database when answering a user’s question.Guardrails
Rules and filters that keep AI systems from producing harmful or off-topic content—e.g., blocking personal data leaks or hate speech.Hallucination
When an AI confidently produces information that isn’t true. Example: Inventing a science-paper citation that doesn’t exist.Hugging Face
A popular platform and community for sharing AI models, datasets, and tools. Think “GitHub for machine learning.”Inference
The act of running a trained model to generate predictions or content. Training is cooking the meal; inference is serving each plate.Large Language Model (LLM)
A deep-learning model, usually transformer-based, trained on vast text to understand and generate human-like language (GPT-4, Llama 3).Llama
A family of open-weight LLMs released by Meta (Llama 1, 2, 3) that developers can freely fine-tune and deploy.Machine Learning (ML)
Algorithms that improve at a task through data and experience, rather than explicit rules coded by humans.Multimodal AI
Models that can process and generate multiple data types—text, images, audio, video—within the same architecture.Neural Network
A web of interconnected nodes (“neurons”) inspired by the brain. Layers of neurons learn to detect patterns, from edges to faces to words.OpenAI
The research company behind GPT models, ChatGPT, DALL·E, and the open-source RL library Gym. Its mission: ensure AGI benefits all.Prompt
The input you feed an AI model. It can be a question, an instruction, or a chunk of data to transform.Prompt Engineering
Crafting prompts (structure, wording, examples) to reliably get the model output you want. Example: “You are a friendly tutor. Explain in three bullet points.”Reasoning Models
Advanced LLM variants optimized to break problems into steps, analyze facts, and produce logically consistent answers.Retrieval-Augmented Generation (RAG)
A workflow where a model first retrieves relevant documents from a database, then uses them to generate an informed answer—reducing hallucinations.Sam Altman
Co-founder and CEO of OpenAI, previously president of startup accelerator Y Combinator, and a prominent voice on AI policy.Temperature
A setting that controls randomness in text generation. Example: Temperature 0 = deterministic, safer; Temperature 1 = creative, more varied.Token
A chunk of text (often 3–4 characters or one short word) that a model processes. Token limits govern prompt length and cost.Vector Database
A specialized database that stores embeddings and can quickly find items with similar vectors—crucial for RAG or semantic search.Zero-shot Learning
Getting a model to perform a task without any examples, relying only on the prompt’s description. Example: “Translate this sentence to Swahili.”You now know the difference between embeddings and vectors, RAG and reasoning models, AGI and ASI. If you found this glossary helpful, feel free to share it with your colleagues or friends!