You’ve probably been hearing AI terms everywhere—on social media, in meetings, in newsletters, and in every second startup pitch. You may know some of them kind of, vaguely. But if we’re being honest, most of us are nodding through AI conversations while frantically Googling words like “transformer” or “multi-modal.”
This post is your cheat sheet: a plain-English glossary of the most important (and confusing) AI terms you’ll encounter. Whether you’re a founder, marketer, developer, or just curious, this is designed for you.
We’ll keep updating this as new buzzwords emerge. Bookmark it. Share it with friends. And use it to feel smarter in your next team call.
What is Artificial Intelligence? [AI Explained]
1. Artificial Intelligence (AI)
AI refers to computer systems designed to mimic human intelligence—understanding language, recognizing images, making decisions, and solving problems. It learns patterns from vast amounts of data—books, websites, numbers—and then applies these patterns to new situations. While clever, AI isn’t conscious; it doesn’t have feelings—it’s simply good at matching patterns and executing tasks. Ranging from simple spam filters to generative systems like ChatGPT, AI systems enhance productivity and creativity by automating or accelerating complex processes.
2. Transformer
A transformer is the core architecture behind modern AI models like GPT and Claude. Introduced in 2017, it uses a mechanism called “attention” so every word in a sentence is seen in relation to all others, instead of one-by-one reading. This enables deep understanding of context and nuance. Transformers are also highly parallelizable—meaning they process many tokens simultaneously—allowing AI researchers to train extremely large models. Today, nearly all high-performance text, speech, and image models are built on transformer foundations.
3. Large Language Model (LLM)
LLMs are AI models that specialize in text. Trained on enormous datasets—books, articles, code—they’ve learned grammar, facts, and reasoning patterns. They predict the next word in a sequence, enabling tasks like summarization, translation, and conversation. The “large” refers to billions of internal settings (“weights”) tuned during training. While originally text-only, today’s LLMs are often multi-modal, meaning they can also work with images, audio, and more.
4. Prompt Engineering
Prompt engineering is the art of asking AI the right way. A prompt is the question or instruction you give—how it’s worded matters a lot. You can direct tone, style, length, or format by the way you ask. For example: “Explain in three bullet points” vs. “Write a detailed essay” will yield different outputs. As AI becomes central to workflows, prompt engineering is becoming a critical skill—almost like programming, but using natural language.
5. RLHF (Reinforcement Learning from Human Feedback)
RLHF teaches AI to align with human preferences. First, people rank or rate different model responses. Then a “reward model” learns from this ranking. The AI is further trained using reinforcement learning to generate responses that maximize these rewards. The result: safer, more helpful models that don’t just predict text but align with what humans consider good answers. RLHF is essential for making AI behave responsibly and usefully.
6. RAG (Retrieval‑Augmented Generation)
RAG enhances language models by letting them fetch real-time information from external sources—like documents, databases, or the web—and use that context when generating responses. This way, the model doesn’t hallucinate answers—it’s grounded in fresh, accurate data. RAG is widely used to build reliable AI assistants that can provide up‑to‑date and precise information.
7. Multi‑Modal
Multi‑modal AI models can interpret and generate across different types of data: text, images, audio, and video. This lets you, for example, show a photo and ask the model to describe it or narrate what’s happening. Multi-modal systems like GPT‑4o seamlessly integrate visuals, audio, and text, making them flexible and context-aware—able to handle a richer mix of inputs in a single conversation.
8. Embedding
An embedding is a numeric representation of text or images capturing its meaning. Similar words or concepts get similar embeddings. For instance, “cat” and “kitten” will be close together in the embedding space. Embeddings are key to search, recommendations, clustering, and RAG—enabling models to find relevant information, group similar ideas, or measure semantic similarity efficiently.
9. Context Window
A context window is how much text an AI can process at once. Older models handled a few thousand tokens (a few pages). Modern ones can take tens or hundreds of thousands of tokens—long documents, whole books, or extended conversations. Larger context windows let AI remember more, maintain coherence, and reference earlier parts of long inputs without losing track or truncating important info.
10. Hallucination
In AI, hallucination means confidently producing incorrect or made-up information. The model might cite fake facts or invent quotes—but it doesn’t know it’s wrong. Hallucination happens because AI generates plausible-sounding responses based on patterns, not verified truth. Reducing hallucination is a major focus of AI reliability efforts—through grounding via RAG, uncertainty indicators, or model fine-tuning.
11. Zero‑Shot / Few‑Shot Learning
Zero‑shot learning is when a model performs tasks it was never explicitly trained for—without any examples—just by interpreting the prompt. Few‑shot learning gives the model a few examples first. Both demonstrate the flexibility of LLMs: you don’t need extensive re-training to teach them new tricks—just effective demonstration or instruction via prompts.
12. Inference
Inference is the act of an AI model using what it’s learned—like when ChatGPT responds to your query. It’s the real-time request/response interaction. Training is when models learn patterns. Inference is when they apply that learning to generate a response—predicting the next token, completing your sentence, answering your question, or creating content.
13. AI Agents
AI agents are models that don’t just talk—they act. They can use tools, fetch data, send emails, or schedule meetings on your behalf. Driven by protocols like MCP, agents integrate LLMs with real-world actions—autonomous email drafting, workflow automation, or CRM update. They’re like virtual assistants that think, decide, and do—based on your instructions or goals.
14. Autonomous / Multi‑Agent Systems
These are systems where multiple AI agents work together—each with specialized roles. Think of one agent that summarizes customer chats, another that drafts follow-ups, and a third that schedules meetings. They communicate, collaborate, and build on each other’s outputs to achieve complex tasks—much like a human team.
15. Synthetic Data
Synthetic data is artificially generated models of real-world data—like fake but realistic photos, text, or numbers. When real data is scarce or private, synthetic data lets you train models, test tools, and preserve privacy. It mimics distributions of real data while reducing cost and risk—especially useful in healthcare, finance, or security.
16. Overfitting
Overfitting happens when a model learns its training data too well—including noise and specifics—so it performs poorly on new data. It “memorizes” rather than understands. Think of a student who memorized specific homework problems but can’t solve a new, slightly different one. Balancing training with validation helps prevent overfitting.
17. Open‑Source Models
Open-source AI models provide their code, architecture—and often weights—for anyone to use, modify, and deploy. Examples include Meta’s LLaMA, Mistral, and EleutherAI’s GPT-J. These models offer transparency, privacy (you can run them on your own servers), and flexibility—but may lag behind closed-source giants like GPT‑4 in raw performance.
18. Weights
Weights are internal parameters—tuneable knobs—that determine how a model processes inputs. During training, each correct or incorrect prediction tweaks these weights. Over millions or billions of examples, they converge to values that capture patterns in data. Weights are the essence of model knowledge—like a map of learned insights.
19. Token
Tokens are the basic processing units models use—words, subwords, characters, or punctuation. They break text into manageable pieces. For example, “elephant” might be one token, or split into “elep”, “hant”. Models have limits on how many tokens they can process at once—and pricing is often based on total tokens used, both input and output.
20. Training / Pre‑training
Training (or pre-training) is the foundational learning phase where models digest massive datasets and predict next tokens. They learn grammar, facts, reasoning, and patterns by adjusting weights. Training large models takes heavy compute and time—weeks and millions of dollars in GPU resources. In practice, huge foundational models are pre-trained before being used for various tasks.
21. Fine‑Tuning
Fine-tuning means taking a pre-trained model and training it further on specialized data—like customer support chats or legal documents. This tailors the model’s responses to your domain, tone, or facts without losing general language knowledge. It’s like giving a generalist model to a finishing school in your field.
22. Post‑Training
Post-training covers everything done after initial training: fine-tuning, RLHF, safety alignment, bias adjustments, and domain-specific tweaks. It prepares models for real-world deployment—making them safer, more accurate, compliant, and fit-for-purpose in your context.
25. Model Context Protocol (MCP)
MCP is an emerging standard that enables AI models to work with external tools—your calendar, CRM, messaging, or databases—through well-defined APIs. It allows models to generate actionable outputs (e.g., “book a meeting on Tuesday”) that integrate directly into apps and workflows. MCP is gaining traction alongside other protocols like Google’s A2A and IBM’s ACP.
📝 Final Thoughts
You’re now equipped with the essential AI vocabulary. Each term is explained clearly and accompanied by a quick video so you can dive deeper into the concepts and mechanics behind them. Use this blog as a reference—whether you’re reading the latest AI news, crafting a prompt, or building a product.
Bookmark it, share it with your team, and keep it open during meetings filled with AI jargon. You’ll not just keep up—you’ll lead the conversation.