Марія Бровінська AI Eng 15 April 2026, 09:15

Are you tired of the word LLM too? Here's a simple, no-bullshit explanation of key AI terms from dev.ua

If it seems to you that in the last few years everyone has suddenly started speaking some new language, you’re wrong. Strange words keep popping up at rallies, in job postings, and even in regular work chats: LLM, inference, tokens, hallucinations, agents.

And the problem isn’t even that they’re complicated. It’s that they’re often used as if everyone already understands everything. We explain what these wonder terms are and how to understand them.

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What is an LLM and why is everyone talking about them?

LLM is essentially what you’re talking to when you open any modern AI chat. It’s a large language model that has «read» a huge amount of text and learned to notice patterns in language.

When you ask her a question, she doesn’t search the knowledge base for the right answer. She generates the most likely continuation of your query. That’s why sometimes she seems intelligent, and sometimes she says complete nonsense.

The approach itself is called deep learning. Thanks to it, the model does not just follow instructions, but learns from examples and mistakes.

Inside this system are weights — numerical parameters that determine what is more important and what is not. In essence, this is the «memory» of the model.

AGI, multimodality and where it’s all going

AGI is often mentioned separately — it’s a kind of conditional «holy grail» of the industry. The idea is to create an AI that can perform most tasks no worse than a human. The problem is that even within the industry itself there is no single definition of what that means.

Instead, the more real story today is multimodal AI. That is, models that work not only with text, but also with images, video, and sound. One interface — many types of data.

Why AI «makes up» and it’s normal (almost)

This nonsense has a special name — hallucinations. In practice, this means that the model is making things up: it can refer to a fictional study, come up with a fact, or give a very confident but wrong answer.

And most importantly, she doesn’t know she’s wrong. For her, it’s just another «probable» option. That’s why all services ask you to check your answers, even if they sound convincing.

To fix this, RAG is used — an approach where the model pulls real data from databases or the Internet before responding.

In parallel, there is a whole direction of alignment — an attempt to make the behavior of models safer and more predictable.

How the model learns: training, fine-tuning and distillation

Before starting to respond, the model undergoes training on huge data sets.

But almost no one trains models from scratch. Instead, they use transfer learning — they take a ready-made model and adapt it. This refinement is called fine-tuning. And if you need to make the model cheaper — they use distillation: they «pour» the knowledge of a large model into a smaller one.

What you’re really paying for: what are tokens?

When it comes to paid AI, the word “ tokens ” comes up very quickly. It is through them that money is written off.

To put it simply, tokens are pieces of text that the model processes. The longer your query and the longer the response, the more such pieces are needed. And therefore, the more expensive it is.

There is also a context window — the amount of text that the model can «remember» within a dialogue. If you go beyond this limit, it starts to forget previous parts of the conversation.

What happens when a model «thinks»?

When you write a query and wait for a response, a process called inference is triggered. This is when the model actually does its work: it analyzes the query and generates a response.

This is where the speed of the service comes into play. If everything is slow, it’s a matter of infrastructure and load, not just the «smartness» of the model.

At this point, embeddings work — special vector representations that allow the model to understand the meaning of words, not just their form.

And to make the answers more accurate, sometimes they use a chain of thought — a model that «thinks in steps,» like a person solving a problem on paper.

Before it can start responding to queries, the model goes through a training phase — learning on huge datasets. This is a complex and expensive process that only large companies can afford.

That’s why most products work differently: they take an already-made model and train it for specific tasks. This is called fine-tuning. This is how AI solutions for medicine, law, or support appear.

Why do models «think in steps»?

To make the answers more accurate, an approach called chain of thought is sometimes used. In this case, the model does not answer immediately, but rather breaks the task into several steps and goes through them sequentially.

This takes more time, but the result is usually better — especially in tasks that involve logic or calculations.

Why it is important to be able to formulate requests correctly

This is where a separate skill emerged — prompt engineering. This is the ability to clearly explain to the model what you want from it.

There are even zero-shot and few-shot approaches. In the first case, you simply state the problem, in the second, you add examples so that the model better understands what is expected of it.

And there is also a system prompt — these are «invisible instructions» that set the behavior of the model even before you start the dialogue.

Image generation, cache and a little magic

If we talk about images, diffusion works here — a technology that literally collects images from noise.

To make everything work faster, a memory cache is used — the model saves some of the calculations and does not recalculate them every time.

A separate class of models — GANs — works as a competition between two neural networks, which allows you to create very realistic content.

What are AI agents and why is there so much talk about them?

AI agents are an attempt to take the next step. If a regular AI answers questions, then the agent should perform actions.

For example, finding something, booking something, assembling something, or even writing code and running it. It sounds like a logical evolution, but so far this technology is still forming, and there is much more hype around it than stable solutions.

What’s behind it all: compute

All this magic doesn’t work on its own. Behind it is what the industry calls compute — computing power. We’re talking about servers, graphics cards, data centers — all the infrastructure that allows models to learn and work. And today, this is one of the key resources in the world of AI: more power means more possibilities.

And here the shortage begins. So much so that the term RAMageddon has even emerged — when there is not enough memory due to demand from AI companies.

Another trend is open weights. Companies open access to models, but not to all the details of their creation.

Why does everyone throw these words around so easily?

As a result, there is a feeling that AI is something very complex and almost impossible to understand. When in fact, most of these terms describe quite simple things.

The problem is not in the words themselves, but in how they are used — often without explanation and with unnecessary pathos.

But there is good news: you don’t need to be an AI researcher to navigate this topic. All you need is to understand the basic principles and not be afraid to ask simple questions.

Because it quickly becomes clear who really understands what they’re talking about and who’s just repeating buzzwords.

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