The ONLY AI Dictionary You Need: 25+ Key Terms Explained Simply
From "LLM" to "RAG," we'll build your AI vocabulary from the ground up—with examples you'll actually understand.
Let's Learn to "Speak AI"
Feeling lost in a sea of AI buzzwords? One minute someone's talking about "RAG," the next it's "Transformers," "hallucinations," and "vibe coding." It feels like you need a special decoder just to follow the conversation.
The truth is, most of these complex terms are built on a few simple, core ideas.
This post is your decoder. We're going to list terms; we're going to build your knowledge from the ground up, starting with the "brain" and ending with how you "talk" to it.
To help us on our journey, here’s a fantastic visual map of all the terms we'll be covering.
Ready? Let's start with the big picture.
Part 1: The Core Concepts (The "Brain")
These are the big, foundational ideas.
Artificial Intelligence (AI):
What it is: The main, overarching idea of a computer system that can perform tasks that normally require human intelligence.
Simple Example: A GPS app finding the fastest route through traffic is AI. It's making a smart decision.
Machine Learning (ML):
What it is: The most common type of AI. Instead of being explicitly programmed with rules, an ML model learns from large amounts of data.
Simple Example: Your email's spam filter. It wasn't programmed with a list of "spam words"; it learned what spam "looks like" by analyzing thousands of examples you and others marked as spam.
Deep Learning (DL):
What it is: A more advanced type of Machine Learning that uses "neural networks" to solve complex problems. It's the engine behind today's most powerful AI.
Simple Example: The AI that creates "deepfake" videos or the system in a self-driving car that identifies pedestrians, stop signs, and other cars.
Neural Network:
What it is: A computer system inspired by the human brain. It's made of interconnected "neurons" (nodes) in layers that process information.
Simple Example: Think of it as a team of specialists. The first layer spots simple shapes (lines, curves), the next spots combinations (an eye, a nose), and the final layer puts it all together to declare, "That's a cat!"
Part 2: How AI Learns (The "School")
ML models have to be "trained." Here’s how they go to school.
Training:
What it is: The process of "teaching" an AI model by feeding it massive amounts of data.
Simple Example: Giving a model 10,000 photos labeled "cat" and 10,000 photos labeled "dog" until it learns the difference.
Supervised Learning:
What it is: Training with an "answer key." The data is already labeled with the correct answer.
Simple Example: This is that cat/dog photo training. Each image has a label ("cat" or "dog"), so the model is "supervised" as it learns.
Unsupervised Learning:
What it is: Training with no answer key. The AI's job is to find its own hidden patterns in unlabeled data.
Simple Example: A shopping site using unsupervised learning to analyze your purchase history and group you with "customers who also like sci-fi books and coffee," all without any human labels.
Reinforcement Learning:
What it is: Learning through trial and error, like training a pet. The AI gets "rewards" for good actions and "penalties" for bad ones.
Simple Example: An AI learning to play chess. It gets a +1 reward for winning a game and a -1 for losing, slowly teaching itself the best strategies over millions of games.
Part 3: The New Wave (Generative & Language Models)
This is the technology that powers tools like ChatGPT and Midjourney.
Generative AI (GenAI):
What it is: A type of AI that can create new content (text, images, code, or music) instead of just analyzing data.
Simple Example: Asking an AI to "draw a picture of an astronaut riding a horse on the moon." It generates a new image from scratch.
Foundation Model:
What it is: A massive, powerful, pre-trained AI that can be adapted for many different tasks.
Simple Example: GPT-4 is a foundation model. It wasn't just trained for one thing. You can use it to summarize text, write a poem, or explain a scientific concept.
LLM (Large Language Model):
What it is: A foundation model that is specialized in understanding and generating text. This is the heart of all modern chatbots.
Simple Example: OpenAIs GPT-4, Google's Gemini 1.5 Pro, and Anthropic's Claude Sonnet 4 are all LLMs.
Part 4: Inside the LLM (The "Building Blocks")
A few key terms for how an LLM actually works.
Transformer:
What it is: The revolutionary AI architecture that powers all modern LLMs. Its secret weapon is "self-attention," which lets it track the context of words across long sentences. A transformer make uses of tokenization and embedding
Simple Example: In "The bee landed on the flower because it wanted nectar," a Transformer model knows "it" refers to "the bee," not "the flower."
Tokenization:
What it is: The first step for an LLM. It's the process of breaking a sentence down into smaller pieces (tokens), which can be words or parts of words.
Simple Example: The sentence "I love AI" might be tokenized into three pieces: ["I", "love", "AI"]. "Tokenization" would be ["Token", "iza", "tion"].
Embedding:
What it is: Turning tokens (words) into a list of numbers (a vector). This allows the AI to "understand" the relationships and meanings of words mathematically.
Simple Example: The numbers for "King" and "Queen" would be very similar. In fact, the AI learns that "King" - "Man" + "Woman" = "Queen".
Part 5: Talking to AI (The "Conversation")
Now that we have our model, here's how we interact with it.
Prompt Engineering:
What it is: The art and science of crafting the perfect question (prompt) to get the best possible answer from an AI.
Simple Example: Instead of "Tell me about RAG," a better prompt is "Explain RAG to me like I'm a 5th grader, using a simple analogy."
RAG (Retrieval-Augmented Generation):
What it is: Giving an LLM an "open-book exam." RAG lets the model "look up" fresh information from an external source (like your company's documents or a website) before it answers your question.
Simple Example: When you ask a bot, "What's the latest news on Project X?" RAG lets it retrieve the latest project update email and then summarize it for you.
Chain of Thought (CoT):
What it is: A prompting trick where you ask the AI to "think step-by-step." This forces it to slow down and show its reasoning, which dramatically improves its accuracy on complex problems.
Simple Example: Instead of just asking for the answer to a math problem, you'd say, "Solve this step-by-step, explaining your logic at each stage."
Part 6: Behavior & Safety (The "Ghosts in the Machine")
AI isn't perfect. Here are the terms for when it goes right—and wrong.
Hallucination:
What it is: When an AI confidently makes up facts. It's not lying (which implies intent), but rather "dreaming up" plausible-sounding information because it doesn't know the real answer.
Simple Example: Asking an AI to cite a legal case and it invents a completely fake (but realistic-sounding) case name and precedent.
AI Alignment:
What it is: The critical field of research focused on ensuring AI systems follow human values and intentions.
Simple Example: Making sure an AI designed to "cure cancer" doesn't decide the easiest way to do that is to harm healthy people in its experiments. It's about making AI helpful and safe.
Explainability (XAI):
What it is: The ability to understand how an AI model made its decision. This is crucial for high-stakes fields like medicine.
Simple Example: A bank's AI denies you a loan. Explainability means the AI can tell you why—e.g., "The decision was based on a low credit score and high debt-to-income ratio."
Part 7: The "Tools" of the Trade
A few final terms you'll see everywhere.
GPU (Graphics Processing Unit):
What it is: A special computer chip that is perfect for the type of math required to train and run large AI models. They are the workhorses of the AI revolution.
Simple Example: Originally made for video games, a GPU can do thousands of simple calculations at the same time, which is much faster than a normal CPU for AI.
Chatbot:
What it is: A computer program designed to simulate a conversation with a human user.
Simple Example: ChatGPT or the little pop-up bot on a shopping website that asks, "Can I help you find something?"
Computer Vision:
What it is: A field of AI that enables computers to "see" and interpret information from images and videos.
Simple Example: Your phone's camera identifying a face to unlock, or a smart car identifying a stop sign.
Part 8: The Bonus Glossary (Completing the Map)
You’ve got the main concepts down! Here are the definitions for the other key terms on our visual map, completing your AI vocabulary.
Fine-tuning:
What it is: Taking a large, pre-trained model (like GPT-4) and training it a little more on a specific, smaller dataset to make it an expert on that topic.
Simple Example: Teaching a general-purpose model to be a legal expert by fine-tuning it on thousands of legal documents.
Parameters & Weights:
What it is: The internal variables and numbers that the AI learns during training. These are the "memories" or "knowledge" of the model.
Simple Example: Think of these as the millions of tiny adjustments to all the connections in a neural network, representing all the patterns it has learned.
Transfer Learning:
What it is: A technique where a model "transfers" knowledge from one task to another, which is much faster than training from scratch.
Simple Example: A model trained to recognize cats has already learned to identify shapes, fur, and eyes. It can transfer this knowledge to learn how to recognize dogs much faster.
AI Model:
What it is: The final, trained AI "brain" that is ready to be used for a specific task (like gpt-4o or claude-3-opus).
Simple Example: If the neural network is the design of the engine, the "AI Model" is the final, fully-built engine that has been tuned and is ready to run.
TPU (Tensor Processing Unit):
What it is: Google's custom-built computer chip (like a GPU) that is hyper-specialized for running AI math at incredible speeds.
Simple Example: If a GPU is a high-performance race car engine, a TPU is a custom-built Formula 1 engine designed for only one thing: winning the AI race.
Compute:
What it is: The raw processing power (usually from GPUs or TPUs) needed to train and run AI.
Simple Example: This is the "horsepower" of AI. Training a big model requires a warehouse full of GPUs, which is a massive (and expensive) amount of compute.
AGI (Artificial General Intelligence):
What it is: A hypothetical future AI that could understand, learn, and apply intelligence across any task a human can, not just specific ones.
Simple Example: This is the sci-fi stuff, like "Data" from Star Trek. We are not there yet!
AI Agents:
What it is: An AI that can do more than just talk. It can make plans, use "tools" (like browsing the web or using your calendar), and take actions to complete a goal.
Simple Example: An AI agent you tell, "Book me a flight to New York for next Tuesday," and it actually browses flight sites, finds the best one, and books it for you.
NLP (Natural Language Processing):
What its is: The broader field of AI that deals with computers understanding and processing human language.
Simple Example: This includes things like translation, grammar checking, or analyzing the "sentiment" (positive or negative) of a product review.
Reasoning Model:
What it is: An AI model designed to "think logically" and solve problems that require planning, deduction, or step-by-step reasoning.
Simple Example: An AI that can solve a complex logic puzzle or plan the steps needed to build a piece of furniture from a manual.
AI Wrapper:
What it is: A simple application or interface that "wraps" around a powerful AI model to make it easier to use for a specific purpose.
Simple Example: A website that only summarizes legal documents. It's a simple "wrapper" that feeds your text to a big LLM with a "summarize this legal doc" prompt.
Context:
What it is: The short-term memory of a chatbot. It's the information (like your previous questions) that the AI "keeps in mind" to have a coherent conversation.
Simple Example: When you ask, "What about him?" and the AI knows "him" refers to "Elon Musk" from your previous question. That's context.
Ground Truth:
What it is: The "answer key." A set of high-quality, verified, and accurate data used to train or evaluate an AI model.
Simple Example: To train a medical AI, the "ground truth" would be thousands of X-rays that have already been labeled by expert human radiologists.
Inference:
What it is: The process of using a trained AI model to make a prediction or generate an answer.
Simple Example: "Training" is like studying for the test. "Inference" is the moment you are actually taking the test (or answering a user's prompt).
Vibe Coding:
What it is: A slang term for "coding" with an AI by describing the feeling or "vibe" you want, rather than writing exact, formal code.
Simple Example: Telling an AI, "Make this button pop more... give it a 'bouncy' and 'fun' animation."
MCP (Model Control Protocol):
What it is: A proposed standard (like HTTP for the web) that would allow AI models and agents to communicate and share data with each other in a standard way.
Simple Example: This is a future-looking idea. Think of it as creating a "common language" so an AI travel agent can automatically talk to an AI calendar agent.
Conclusion: You're Now an AI Insider!
That's it! You've just built a complete vocabulary, from the core "brain" of a neural network to the "conversational" art of prompt engineering.
You may not be an AI developer, but you can now join the conversation, ask the right questions, and, most importantly, understand what's really happening behind the hype.
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