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How AI Language Models Work, in Plain Language

By the Chundu Laboratories team · 4 min read · Updated 24 October 2026

Chat assistants like Jivra can write, explain, translate and answer questions in a way that feels almost human. Behind that, the core idea is surprisingly simple. Understanding it helps you get better results and know when to be careful.

The core idea: predicting the next piece of text

A large language model, or LLM, is a computer program trained to predict what text comes next. Text is broken into small pieces called tokens; a token can be a whole word, part of a word or a punctuation mark. Given some text, the model calculates which token is most likely to follow, adds it, and repeats, one token at a time, until the answer is complete.

That sounds too simple to produce a useful assistant. The trick is scale. To predict text well across billions of examples, a model has to pick up grammar, facts, styles of writing and patterns of reasoning, because all of those help it guess what comes next.

What the model is made of

Modern language models are neural networks, mathematical systems loosely inspired by the brain. They contain a very large number of adjustable numbers called parameters. A small model might have one or two billion parameters; the largest models are believed to have hundreds of billions or more. Most of today's models use a design called the transformer, introduced by researchers in 2017, which is good at working out how each word in a passage relates to the others.

How it is trained

Training usually happens in stages.

• Pre-training: the model reads an enormous amount of text and repeatedly tries to predict the next token. Each time it is wrong, its parameters are nudged slightly to make it a bit more accurate. This takes huge amounts of computing power.
• Fine-tuning: the model is trained further on examples of helpful conversations, so it learns to follow instructions and answer questions instead of just continuing text.
• Feedback and safety training: people, or other trained systems, rate answers, and the model is adjusted to prefer replies that are helpful, honest and safe.

A common technique for small models is distillation, where a smaller "student" model learns from the answers of a larger "teacher" model. Jivra is improved this way using open-licence models.

Why models sometimes make things up

A language model does not look facts up in a database by default. It generates text that fits the patterns it learned. Usually that produces correct information, but sometimes it produces something that sounds right and is false, such as a fake quotation, a made-up book or a wrong date. This is often called a hallucination. It is more likely with rare topics, very recent events, exact numbers and smaller models.

Models can also be inconsistent: ask the same question twice and you may get different wording or even different answers, because there is a little randomness in how the next token is chosen.

What models are good and bad at

Generally good at:

• Writing and rewriting text in different tones and languages.
• Explaining common concepts and summarising text you provide.
• Brainstorming and organising ideas.
• Helping with common coding tasks.

Generally weaker at:

• Recent news, unless connected to a search tool.
• Exact arithmetic on long numbers, unless they work step by step or use a calculator tool.
• Niche facts, citations and anything requiring guaranteed accuracy.
• Knowing what they do not know.

Does the model understand?

Researchers still debate what "understanding" means for these systems. In practice, it is safest to think of a model as an extremely well-read pattern engine: impressive and useful, but without personal experience, intentions or a guaranteed grip on truth.

Using that knowledge

• Give clear context, since the model works only from what is in the conversation and what it learned.
• Ask it to work step by step for maths and logic.
• Verify important facts with reliable sources.
• Paste the actual text you want summarised or checked instead of relying on its memory.
• Never share passwords, OTPs or private information.

Open models and why they matter

Some companies keep their models closed. Others release open models whose weights anyone can download, study and run, under a licence. Jivra is built only on open models and runs on Chundu Laboratories' own servers. Open models let smaller teams, researchers and countries build their own AI tools, customise them for local languages and keep control over their data.

Spotted a mistake? Email hello@chundulabs.in. Have a question? Ask Jivra.