The kind of AI behind chat assistants, explained without the jargon: what it is, how it produces text, and where it falls short.
LLM stands for large language model. It is the technology behind chat assistants like ChatGPT, Claude, and the models Berges AI runs. At its core it does one deceptively simple thing: given some text, it predicts what text should come next.
That is the whole trick. Everything a chat assistant appears to do, answering questions, writing an email, explaining code, comes out of predicting the next piece of text again and again until the response is finished.
A model does not read or write whole words. It works in tokens, which are word fragments. It looks at everything so far and assigns a probability to every possible next token, picks one, adds it, and repeats. Fluent paragraphs are the result of doing this thousands of times very fast.
These models are large in two ways: the amount of text they trained on, often much of the public internet, and the number of internal values, called parameters, that store what they learned. Modern models have tens or hundreds of billions of parameters. Scale is what turned next-word prediction from a party trick into something genuinely useful.
A base model does not look anything up. Its "knowledge" is patterns baked into its parameters during training, frozen at the point training stopped. That is why it has a knowledge cutoff and why, without extra tooling, it cannot tell you today's news. Techniques like retrieval and tool use are how you give it fresh or private information.
The model optimizes for plausible-sounding text, not for truth. When it does not know something, it does not go quiet, it generates the most likely-looking answer anyway. That failure mode is called hallucination, and it is the single most important thing to keep in mind when you use one.
Think of an extremely well-read autocomplete. Phone autocomplete guesses your next word from a little context. An LLM guesses the next fragment from everything written so far, having absorbed patterns from billions of pages. Same idea, vastly more context and training.
Berges AI runs several of these models and adds a thin layer on top, called interceptors, that shapes how they respond: less filler, clearer reasoning, more honest about uncertainty. The model does the predicting; the layer keeps it useful.
Try Berges AINo. AI is the broad field. An LLM is one kind of AI, focused on language. Image generators, recommendation systems, and self-driving perception are all AI too, but they are not LLMs.
It has no beliefs or awareness. It models statistical patterns in language extremely well, which can look like understanding, but there is no inner comprehension behind it. Useful, but not a mind.
Because it is built to produce likely text, not verified text. When it lacks a real answer it still generates a plausible one. Always check anything factual or high-stakes.