What's behind Gen AI? A deep dive into understanding LLMs (Large Language Models)

2026. Generative AI is no longer a novelty — it's part of how we work, every day. In barely four years, it has reshaped our habits, boosted our productivity, and freed us to focus on what truly matters.

But behind the magic, how does it actually work?

In this module, you'll get a clear, complete understanding of Large Language Models — the core technology powering Generative AI — and why they're changing everything.

The AI Landscape in 2026

As we going into mid-2026, AI is no longer a promise, it is directly in our mailboxes, meetings, and workflows. For example, generative AI has the ability to create drafts for your emails, generations code or summarise data from your recorded meetings.

In 2026, many enterprises are now heading a step further: they look to adopt AI within their internal processes, after a couple of years experimenting.

From a technical perspective, Gen AI's race is still ongoing; massive investments and major partnerships are announced every day. Last week, OpenAI announced the 5.5 version of ChatGPT that brings more features than ever before, according to their statement, along with optimal performance to compete with Claude, their closest competitor.

What is a Large Language Model?

All Generative AI's models mainly rely on LLM technology (Large Language Model). To the contrary of popular belief, an LLM doesn't think, it calculates the most probable next token given everything before it.

As an example, when you prompt ChatGPT where the Eiffel Tower is located? It won't think as the human brain does. It will search the most probable answer: Paris.

This distinction matters more than it seems. An LLM has no understanding, no intent, low memory of your previous conversations. It processes your input — called a prompt — as a sequence of tokens, and generates a response one token at a time, each predicted from the ones before it.

That's what makes LLMs both powerful and limited. They can produce fluent, convincing, and often accurate text. But they can also be confidently wrong — because plausibility and truth are not the same thing.

Limits, biases & responsible use

You've probably seen it before: an AI confidently tells you something that turns out to be completely wrong. A fake court case citation, a product that doesn't exist, a historical date that never happened. This is what the field calls a hallucination — and it's not a bug that will simply be patched away. It's a structural feature of how LLMs work.

The second limit is the context window — the amount of text an LLM can "see" at once. Think of it as working memory. Everything outside that window simply doesn't exist for the model. Feed it a 200-page document when its window only fits 50? It will silently ignore the rest. Ask it to remember something you mentioned three hours ago in a different session? Gone.

An LLM learns from data. And data is written by humans — with all the assumptions, blind spots, and historical inequalities that implies. If the training corpus overrepresents certain voices, geographies, or perspectives, the model will too. This isn't a theoretical concern: studies have documented LLMs producing systematically different outputs based on the perceived gender, ethnicity, or nationality embedded in a prompt.

Why LLMs are changing everything

For decades, interacting with a computer required learning its language: command lines, syntax, structured inputs. LLMs flipped that entirely. For the first time, machines adapt to how humans naturally communicate. You write the way you think, and the system follows.

But the impact goes deeper than productivity. LLMs are changing who can do what. A solo entrepreneur can now produce communications at the quality level of a full marketing team.

This is why the technology feels different — because it compounds. Each person using an LLM effectively becomes, in some tasks, significantly more capable than before. And when that happens at the scale of millions of workers, industries, and institutions simultaneously, the aggregate effect is structural, not incremental.

That said, "changing everything" doesn't mean "replacing everything." LLMs have no judgment, no accountability, no stake in outcomes. They are powerful amplifiers — of good work and of bad. The question was never whether the technology would be transformative. The question is who steers it, and toward what.