AI Glossary

Context window. It controls what a model can 'see' while answering, your prompt, the uploaded file, the chat history, the retrieved documents. Nearly every limitation people complain about (hallucinations, dropped instructions, forgetting halfway through a long task) traces back to context. Once you internalise that the model is reading a scroll with a finite edge, every other term fits into place.

No. The math matters if you're training models, but for using AI, writing prompts, building agents, choosing tools, every term on this page can be understood with a sharp analogy and a concrete example. We spell out when a deeper technical detail genuinely changes your decision (e.g., picking a model with a larger context window for document analysis) and when it's academic.

AI is the field. Machine learning (ML) is the dominant technique inside AI where the machine learns patterns from data instead of being hand-coded. Generative AI is a subset of ML focused on producing new content (text, images, code, video, audio) rather than just classifying or predicting. ChatGPT, Claude, Midjourney, and Sora are all generative AI. A spam filter is ML but not generative. Both are AI.

Wikipedia is comprehensive but written for people who already know the adjacent terms, so every definition links to three more terms you don't know. A textbook is rigorous but assumes weeks of study. This glossary is written the way a senior engineer explains things to a smart generalist at a whiteboard: one clear mental model, one analogy, one worked example, one sentence on when the detail actually matters.

Product names and version numbers decay in months, 'GPT-4' as a term is largely historical now. Architectural labels ('Mixture of Experts', 'Transformer', 'diffusion model') tend to last years. Core concepts (context window, tokens, embeddings, fine-tuning, gradient descent) are effectively evergreen. Trust the concept tier; verify the product tier against the current cycle before citing anything load-bearing.

A token is the unit a language model reads and writes, roughly 4 characters or 3/4 of a word in English. You care because everything about using AI is priced, limited, or slowed down per token. 'The context window is 200,000 tokens' means roughly 150,000 English words fit. 'Output is 2,000 tokens' is roughly 1,500 words or 3 pages. When bills and latency spike, token count is almost always the cause.

A reasoning model (OpenAI's o-series, Anthropic's extended-thinking Claude, DeepSeek R1, Gemini Thinking) spends compute on an internal chain of thought before responding. A regular LLM answers in one pass. Reasoning models are substantially better at math, code, and multi-step planning but slower and more expensive per query. Rule of thumb: use reasoning models for correctness-sensitive work (math, code, legal logic); use regular models for speed and bulk content work.

RAG (retrieval-augmented generation) is the pattern where you fetch relevant documents from a knowledge base and stuff them into the prompt before the model answers, so it cites your data instead of hallucinating. Use RAG when (1) your data changes often, (2) the data is proprietary, (3) you need source citations, or (4) the corpus is too large for the context window. Don't use RAG for general knowledge, the model already knows that.

A workflow is a fixed sequence of steps where an LLM fills in a blank at each step, predictable, debuggable, cheap. An agent chooses its own next step using tools, with a loop that continues until it decides it's done, flexible, unpredictable, expensive. Use workflows for repeatable processes (extract this form, draft this email, classify this ticket). Use agents for open-ended tasks where the right steps are genuinely unknown in advance.

Don't chase the weekly churn. Learn the 30 evergreen terms at the concept tier (context, tokens, embeddings, fine-tuning, RAG, agent, diffusion, transformer, attention, reasoning, distillation, quantization, MoE, RLHF), everything new is a recombination of those. Set a 15-minute weekly read (Simon Willison's blog, Latent Space, the Anthropic and OpenAI release notes) and you'll be ahead of 95% of working professionals.