What is an LLM (large language model)?

What an LLM is.

At its core, an LLM is a text predictor (but a very advanced one). During training, the model learned patterns from enormous amounts of text: how sentences flow, how concepts are related, and what an answer looks like. As a result, it can formulate a response to a question that is accurate and reads naturally, built word by word based on what is most likely to fit.

It’s important to understand that an LLM doesn’t “know” anything in the human sense. It operates on probabilities. That explains both its strength (fluent, useful language on almost any topic) and its weakness (it can assert something incorrect with conviction, a hallucination).

How an LLM Works.

Text is broken down into pieces, called tokens, and the model predicts the next token based on everything that came before it. Do that thousands of times in a row, and a complete answer emerges. The “context” you provide—your question plus any accompanying documents—determines what the model focuses on. That’s also why the way you prompt an LLM matters so much. Good instructions give the model direction, and that’s what prompt engineering is all about. And it’s the reason an LLM only becomes truly valuable when it has access to your own data, rather than just its training knowledge.

Well-known LLMs.

The best-known families are Claude (Anthropic), GPT (OpenAI), and Gemini (Google). They differ in their strengths: one reasons more deeply, another is faster or more cost-effective, and a third handles long documents better. There is no “best” LLM in general—only the best model for a specific task. That nuance is exactly why model independence matters. If you build your software around a single model, you’re locked in when prices change or if another model improves. If you choose based on each use case, you remain flexible.

Cloud versus self-hosted.

You can use an LLM in the cloud via the provider’s API, or run it self-hosted on your own infrastructure . The cloud is often faster and cheaper to get started with and gives you access to the most powerful models. Self-hosting provides maximum control over data and governance, which is important when dealing with strict privacy or compliance requirements. It’s a deliberate decision that depends on each project—not an automatic choice.

Integrating LLMs into software.

An LLM only becomes useful when it’s integrated into your own software and processes, not as a standalone chat bot. That’s when it can answer questions based on your documentation, summarize files, or perform tasks within your systems. That integration, and keeping it secure, is what Cube AI does by building it into existing software.