LLM — Language model
A language model is a system trained on enormous amounts of text that, given a fragment, predicts how it continues. Writing, summarising, translating and holding a conversation all come out of that one simple capability: they are all ways of continuing a text plausibly.
What is language model?
A language model is a system trained on enormous amounts of text that, given a fragment, predicts how it continues. Writing, summarising, translating and holding a conversation all come out of that one simple capability: they are all ways of continuing a text plausibly.
Also: LLM
Predicting text is not knowing
A model does not query a database or verify anything: it produces the most plausible continuation. That explains both why it writes so well and why it invents things with total confidence. Those are the same property, and no amount of training separates them.
Why it matters
What changes in a SaaS
It is the piece that writes, and it is almost never where the problem is. When an AI support system answers badly, the cause is usually what information it was given, not which model wrote it. Swapping models is the first reaction and rarely the fix.
Language model in detail
Context window
How much text it can hold at once: the question, the conversation history and the retrieved documents. When it fills up something has to be dropped, and that choice affects the answer more than the brand of the model.
Temperature
A setting that controls how far it strays from the most likely continuation. High gives more varied text; low, more predictable. Support wants low: nobody wants creativity in the answer about an invoice.
Where it runs matters
If your customers conversations pass through a model hosted outside the European Union, that has data protection consequences you need to be able to explain. It is a question worth asking any vendor before signing.
How Intake handles it
Questions about language model
What does LLM stand for?
Large Language Model. "Large" refers to the size of the model and of the data it was trained on.
Does a language model learn from my conversations?
Not by default, and it depends on the vendor. Using your conversations for training is a contractual decision: check what the contract says rather than assuming either answer.
Which model is best for support?
The question is usually the wrong one. Given the same information, current models write comparably; what changes the outcome is what information they get and which tools they can use.
Related terms
A term on its own is only half understood. These come up in the same conversation.
RAG — Retrieval-augmented generation
Retrieval-augmented generation, or RAG, is the technique of finding relevant information in your own sources and handing it to a language model so it writes the answer with that. The model does not memorise the content: it looks it up at answer time.
Hallucination
A hallucination is an answer from a language model that sounds right and is not: an invented figure, a feature that does not exist, a plausible number nobody checked. It is not a bug, it is a direct consequence of how a model works — it produces the most probable continuation, not the verified one.
Embedding
An embedding is a text represented as a list of numbers that encodes its meaning. Two texts that say the same thing with different words produce similar lists, and that numeric closeness is what makes it possible to search by meaning rather than by exact match.
AI agent
An AI agent is a system that, beyond generating text, reads data and takes actions to finish a task. In support that means it checks the actual state of the customer account before answering, and when the answer involves doing something, it does it instead of explaining how.
Tool calling
Tool calling is the mechanism by which a language model can run functions you define instead of only writing. You describe the available tools, the model decides which one to use and with what arguments, and it receives the result to fold into its answer.
Chatbot
A chatbot is a program that holds a written conversation with a user. In support, current ones index the company documentation and answer from it; earlier ones followed decision trees written by hand.
