I will be honest with you. A few months ago, I kept hearing words like tokens, embeddings, attention mechanisms and nodding along like I fully got it. I didn't.
So I did what any curious person does. I went deep. I started studying AI architecture, how these systems actually work from the backend, and how all these buzzwords connect.
What I found was actually fascinating and way more approachable than I expected. So I am turning it into a series. "20 AI concepts", explained simply.
Here are the first five.
1. Large Language Models (LLMs)
Think of an LLM as a system that has read an almost incomprehensible amount of text: books, articles, code, conversations and learned the patterns of how language works.
It doesn't store facts like a database. It learns how language flows and uses that to predict what comes next. That's why it can answer questions, summarise documents, write code, and hold a conversation. It's not retrieving answers. It's constructing them, word by word, based on patterns it's internalised. Wild when you think about it.
2. Tokenisation
Before any of that can happen, the model needs to convert your words into something a computer can actually work with. That's tokenisation: breaking text into smaller chunks called tokens.
"AI is transforming industries" might become:
AI | is | transform | ing | industries
Sometimes it breaks at word boundaries. Sometimes mid-word. Each token then gets converted into a number, because at the end of the day, models are doing math, not reading.
This is why token limits matter when you are working with AI tools. The model isn't reading your whole document the way you do. It's processing a sequence of numbered chunks.
3. Vectors (Embeddings)
Here's where it gets interesting. Once you have tokens, the model converts them into vectors - lists of numbers that represent meaning, not just the word itself.
The cool part? Words with similar meanings end up mathematically close to each other in this numerical space.
Dog is close to cat. Doctor is close to nurse. Car is close to vehicle.
This is how AI can do semantic search - finding things that mean the same thing, even if the exact words don't match. It's also the foundation of tools like vector databases and RAG systems, which we will get to later in this series.
4. The Attention Mechanism
This one is one of my favourite concepts. Take this sentence: "The dog chased the ball because it was fast." What does "it" refer to? The dog or the ball?
You figured it out instantly. But for a model processing individual tokens, that's actually a hard problem. The attention mechanism solves this. It allows the model to look across the entire sentence (or paragraph, or document) and figure out which words are most relevant to understanding each other.
It's why modern AI doesn't just process text left-to-right like older systems did. It understands relationships between words, even across long distances in a passage.
The 2017 paper "Attention is All You Need" (link: https://en.wikipedia.org/wiki/Attention_Is_All_You_Need ) introduced this as part of the Transformer architecture. Pretty much every major AI model today is built on that foundation.
5. Self-Supervised Learning
Here's something that always blew my mind a little. Training a model used to require humans to label massive amounts of data manually. This image is a cat. This email is spam. This review is positive.
Self-supervised learning flips that. The model trains itself by predicting missing or upcoming words in text.
Fill in the blank: "The capital of France is ____."
Do that billions of times across billions of sentences, and the model starts to pick up grammar, facts, reasoning, even nuance - without anyone manually labelling anything. It's a beautifully simple idea that unlocks an almost unlimited amount of training data.
All of these concepts are the foundation of everything - conversational AI, enterprise copilots, semantic search, AI agents, and autonomous workflows.
When you understand how the building blocks work, the more complex stuff starts to make a lot more sense.
What's Coming Next?
This is Part 1 of a 20-concept AI series. My goal is simple - to make this stuff genuinely understandable for anyone working in or around technology today.
If you're on this learning journey too, follow along. Part 2 is coming soon.
What AI concept has confused you the most? Drop it in the comments — I might just cover it in the series. Let's exchange concepts and ideas.
#AIconcepts #LLM #vectordatabase #tokenisation #attention #supervisedlearning #machinelearning #poc #aiprojects #commercialprojects
No comments:
Post a Comment