This content originally appeared on DEV Community and was authored by Foysal Imtiaz
If you're not upto date with the AI world, you've probably been left with the feeling that everyone's speaking a different language. Jargon like "LLM," "RAG," and "agentic AI" is thrown around as if we all understand it – but let's be honest, we're most likely just smiling and hoping for the best. The world of AI is coming on fast, and these jargons can be daunting. But here's the thing: understanding these buzzwords is not about being smart. These concepts are shaping the way we work, learn, and utilize technology everyday. So, in this blog post, I'm going to decode the top 20 AI terms you need to know, defined in plain English with real-life examples.
1. API (Application Programming Interface)
Imagine an API like a restaurant waiter. You (the customer) don't go into the kitchen to cook your own food. Instead, you tell the waiter what you want, the waiter places that order in the kitchen, and then they bring you back what you asked for. An API accomplishes the same – it's that middleman that enables different software programs to talk with each other. If you use ChatGPT through a website, that website is calling OpenAI's API to relay your queries and get the replies back.
2. LLM (Large Language Model)
An LLM is basically a super-smart computer program that's highly adept at understanding and generating human language. Imagine someone who has read almost everything available on the internet and can compose emails, or answer questions based on it. ChatGPT, Claude, and Google's Gemini are all LLMs. The "large" part refers to the amount of training data that they have been exposed to. You will learn more about the large context later in this article.
3. Hallucination
No, AI is not hallucinating on psychedelic drugs. Hallucination in the context of AI is when an AI confidently provides you with data that seems completely plausible but is totally fabricated. It's similar to that friend who makes up really elaborate stories but is so confident about it that you tend to believe them, even when they're completely wrong. Always verify critical information from AI!
4. Multimodal
This is a fancy way of saying that an AI is capable of processing different types of content – not just text, but pictures, sound, and videos. GPT-4 is multimodal in the sense that you can show it an image and then ask it questions about it.
5. Open Source vs Closed Source
Open source is similar to a cookbook where the ingredients and instructions can be seen by all. Models like Meta's Llama or Stability AI's models are open source. Closed source is similar to Coca-Cola's secret formula. Companies like OpenAI (ChatGPT) and Anthropic (Claude) keep their exact methods secret. Open source enables developers to play around and improve, but closed source is more polished products but less open and transparent.
6. Token
Tokens can be thought of as the AI's way of breaking down language into bite-sized pieces. A token may be a word, a part of a word, or even punctuation. For example, "Hello world!" may be divided into three tokens: "Hello," " world," and "!". It's much like the way you would break a sentence down into individual words when learning a language. This is crucial because the majority of AI services charge per how many tokens you use – higher number of tokens = higher cost.
7. Context Window
Imagine you are in a conversation, but you only remember the last 10 minutes of conversation. That's essentially what a context window is for AI. It is the amount of recent conversation or writing the AI can "remember" and process at once. If you're talking to ChatGPT for hours and then it suddenly forgets what you were talking about previously, you've hit the window of its context. More recent models have massive context windows and some can even remember an entire book's worth of text.
8. Pretrained Models
Think of this as an AI that already attended school before you even came across it. A pretrained model already knows things from huge amounts of text – like having read a million books, articles, and websites. It's like hiring someone who already has a college degree rather than having to train them from scratch. When you use ChatGPT or Claude, you're using pretrained models that already learned general things about the world.
9. Inference
This is the technical term for when an AI is actually processing and delivering you a response. It's similar to when you ask someone a question and they pause for a moment before responding. When you message ChatGPT and it comes back with a response, that is real-time inference. The AI is using all the stuff it learned when it was being trained to determine what it's going to say next.
10. Parameter
Parameters are like the "brain settings" of the AI – millions or billions of minuscule adjustments that control the way the AI behaves and what it's aware of. Think of them as the strength of neuron connections within a brain. If you say "GPT-4 has 1.8 trillion parameters," you're really saying it has 1.8 trillion distinct settings that were adjusted while training. More parameters usually mean a stronger AI, but one that takes more computing power.
11. Prompt Engineering
It's the craftsmanship of communicating to AI in order for you to make the most of it. It is similar to learning how to ask your boss for a raise– it's not so much what you ask, but the way you ask. Instead of "write me an email", a skilled prompt engineer would be able to use the following: "Write a business email to a customer regarding the postponement of a project, using the tone of apology and confidence, in 150 words." The more precise, the better the result.
12. Fine Tuning
Let's say you have a smart friend who knows everything about everything, but you want to turn them into a master chef. Fine-tuning is like shipping that buddy off to culinary school to be an expert. You take a pre-trained AI model and retrain it on specialized data to have it better suited for specific tasks. A company, for example, can fine-tune a general language model on medical reports to create an AI that is especially good at helping physicians. If you see a specialized AI saas on the internet, that is quite possibly fine tuned to perform that specific task.
13. Embeddings
That's how language and concepts are converted by AI to numbers that can be manipulated by computers. It's like giving a GPS location to every word or concept. Words that mean the same thing have locations that are close to one another in this numerical landscape. So "cat" and "dog" would have locations that are close to one another, but "cat" and "asteroid" would be far from one another. This enables AI to understand that "puppy" is nearer to "dog" than to "mathematics."
14. Vector Database
Remember those numerical coordinates we just mentioned for embeddings? A vector database is just a structured file system to store all those coordinates. It is designed to locate information that's about what you are looking for instantly. If you ask "Tell me about cats," the vector database can locate all information about cats instantly because it has access to where the numerical coordinates around the "cat" coordinate are.
15. RAG (Retrieval-Augmented Generation)
RAG is equivalent to giving an AI access to Google when it's answering your questions. Instead of just basing its answer on what it was trained on, the AI can search outside databases or documents to decide what's relevant and then base its answer on that information. This helps to reduce hallucinations and keep things more current.
16. AI Agents
Think of an AI agent as a virtual personal assistant who can actually do something, not just chat. While a typical chatbot can only chat, an AI agent can make travel arrangements, compose email, schedule appointments, or order from the internet. It is the distinction between requesting someone to show you the way and requesting them to drive you there. AI agents can perform in the real world through various apps and services.
17. Agentic AI
This is where AI systems can act autonomously, decide, and perform without persistent human supervision. It is like the distinction between a remote-controlled car (traditional AI that needs constant input) and an autonomous car (agentic AI that can travel from point A to point B independently). Such systems can break down complex tasks into small steps, learn when things don't proceed as planned, and continue towards their ends.
18. MCP (Model Context Protocol)
MCP is a type of universal translator which makes different AI tools and systems interact with each other very well. Just imagine that all the apps on your phone speak distinct languages and are unable to communicate with each other – MCP solves it for AI systems. It creates a uniform way for AI models to fetch and share data among different platforms and services, and everything is interconnected and streamlined.
19. Reinforcement Learning
This is just like training an AI through trial and error, rewards, and punishment. Imagine training a dog to learn tricks – reward them when they do something correct and don't respond when they do something incorrect. Reinforcement learning does the same: the AI tries doing various things, gets feedback on what works well, and gets better over time. This is how ChatGPT was trained to have productive conversations and not create random text.
20. Reasoning Models
These are AI systems specifically designed to think through problems step-by-step, like showing your work in a math class. Instead of just spitting out an answer, reasoning models break down complex problems into logical steps and explain their thinking process. OpenAI's o1 model is a good example, where it may take its time "thinking" over a question before responding, producing more accurate results on hard tasks like coding or scientific queries. Google's Gemini Pro based on chain-of-thought prompting, and study models like PaLM-2 with high performance on multi-step reasoning tasks are a few instances.
Understanding of these buzzwords won't make you an AI expert overnight, but it will definitely make you a breeze to keep pace with as this technology becomes a larger part of our lives. The most important thing to remember is that underneath the jargon, we're really just talking about tools that are becoming more capable of understanding and helping individuals. They range from simple to advanced, but they're all in service of the same exciting process of developing more effective and capable AI systems.
This content originally appeared on DEV Community and was authored by Foysal Imtiaz
Foysal Imtiaz | Sciencx (2025-09-16T13:36:04+00:00) AI Lingo, Decoded: 20 Buzzwords You Need to Know. Retrieved from https://www.scien.cx/2025/09/16/ai-lingo-decoded-20-buzzwords-you-need-to-know/
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