If you’ve ever nodded along to an AI conversation while quietly wondering what half the words meant, this page is for you. Below are the AI terms people actually ask about, each defined in a sentence or two of plain English. No math, no jargon. When you want the longer, friendlier version, every term links to the full episode that explains it.
How AI actually works
Large Language Model (LLM). The kind of AI behind ChatGPT, Claude, and Gemini. It learned from an enormous pile of text and answers by predicting the next most likely word, one piece at a time, which is why it can sound fluent and still be wrong. How does ChatGPT actually work?
Training data. The text, images, and code an AI learned from. Everything the model knows, and every bias it absorbed, traces back to this pile. What is training data?
How AI learns from data. Models aren’t programmed with rules. They’re shown millions of examples and gradually adjust themselves until their guesses get good. How does AI learn from data?
Tokens. The small chunks of text an AI reads and writes in, usually word-pieces rather than whole words or letters. The model sees tokens, not spelling, which explains a lot of its odd mistakes. What are AI tokens?
Synthetic data. AI-generated data used to train other AI. Useful, but lean on it too hard and models start drifting on a feedback loop of their own output. The synthetic data feedback loop
Memory, context, and limits
Context window. How much of the current conversation an AI can keep in mind at once. Go past the limit and the earliest parts quietly drop off. AI memory and context windows
AI memory. Most chatbots don’t truly remember you between sessions. What feels like memory is either notes saved on the side or the conversation still sitting inside the context window. How AI memory works
Why AI is bad at math. It predicts the look of an answer rather than calculating it, so it can nail a hard concept and fumble simple arithmetic. Why is ChatGPT bad at math?
Inside the model
Weights. The billions of adjustable numbers where a model stores everything it learned. They’re also why even the people who built an AI can’t fully read what’s inside it, the “black box” problem. Can AI explain itself? Weights and the black box
Fine-tuning. Taking a finished, general model and training it a little more on focused examples so it specializes, without rebuilding it from scratch. What is fine-tuning an AI model?
Checkpoints and versions. Saved snapshots of a model frozen at a moment in time. It’s why an update can make a familiar AI suddenly “feel different.” Where do old AI models go?
Distillation. Training a smaller, cheaper model to imitate a bigger one. You get something faster and lighter, but it’s a copy of the behavior, not the original model. Can you copy an AI? Distillation explained
Memorization. Sometimes a model stores and repeats exact bits of its training data word for word, instead of only generalizing from it. That’s a real privacy and accuracy risk. Does AI memorize its training data?
Machine unlearning. Deliberately making a model forget specific information. Genuinely hard, because the data is spread across billions of weights instead of filed in one spot. Can you make an AI forget?
System prompt. The hidden instructions an AI receives before you type a word, setting its personality, its rules, and what it won’t do. What’s a system prompt?
Emergent abilities. Skills a model was never directly taught that appear once it’s large enough. Surprising, useful, and not fully predictable. How AI develops abilities it was never taught
RAG (Retrieval-Augmented Generation). Giving an AI the ability to look things up in a trusted source before answering, instead of relying only on what it memorized. It’s one of the main ways AI gets more accurate and current. What is RAG?
Why AI gets things wrong
Hallucination. When an AI confidently states something untrue. It happens because the model is predicting plausible-sounding words, not checking facts. Treat any answer that matters as unverified until you check it. What are AI hallucinations? · Why does AI sound so confident when it’s wrong?
Why AI won’t say “I don’t know.” Models are built to produce an answer, so they tend to guess fluently rather than admit uncertainty. Why ChatGPT won’t say “I don’t know”
AI bias. Because models learn from human-made data, they absorb human biases and can repeat them at scale. AI bias and fairness
AI “emotions.” When a chatbot sounds sad or excited, it’s matching the pattern of emotional language in its training data, not experiencing feelings. Are AI emotions real?
Using AI well
Writing better prompts. Clear, specific instructions with examples get dramatically better results than vague ones. How to write better ChatGPT prompts
Fact-checking AI. Treat AI answers as a confident first draft, not a source. Verify anything that matters. AI for fact-checking and its limits
Detecting AI-written content. Spotting AI text is harder than the tools claim, and the “detectors” are unreliable. How to detect AI-written content
Writing with AI without sounding like AI. The trick is using it for structure and ideas while keeping your own voice on top. Using AI for writing without sounding like AI
Using AI to organize your thinking. One of its most underrated jobs: turning a messy brain-dump into something with shape. Using AI to organize your thoughts
Learning with AI, not just leaning on it. Used well it’s a tutor; used lazily it quietly erodes the skills you wanted to build. How to learn with AI · How to use AI without losing your skills
AI for research. Good for fast orientation on a topic, risky as a final authority. Best AI tools for research
AI for coding. Genuinely helpful for boilerplate and explanations, still needs a human who can check the work. ChatGPT for coding: what it’s good at
Generation and tools
AI image generation. Tools like DALL-E, Midjourney, and Stable Diffusion build pictures by starting from noise and refining toward your description. How does AI image generation actually work?
How AI designs interfaces. A look at why AI-built layouts often feel same-y, and what that says about how these models work. How AI designs interfaces without even trying
The bigger questions
Can AI replace my job? The honest answer is task-by-task, not job-by-job, and it varies enormously by role. Can AI replace my job?
Is paying for AI worth it? When the premium tiers earn their keep and when the free tools are plenty. Cost of using AI: is premium worth it?
Why AI feels scary. A calmer look at where the fear comes from, what Hollywood gets wrong, and which risks are actually worth paying attention to. Why AI feels scary (and why it shouldn’t)
New terms get added here as the show explains them. Bookmark it as your plain-English cheat sheet, and if there’s an AI word you keep tripping over that isn’t here yet, it’s probably a future episode.
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