Behind every AI assistant is a language model. Knowing how it works immediately explains where things go wrong.
An LLM, large language model, is software that is trained on enormous amounts of text and predicts which word follows logically. This allows it to answer questions, summarize and write. It doesn't understand anything in the human sense: it recognizes patterns, which explains why it sounds convincing and sometimes says nonsense.
IN COMMON LANGUAGE
Predict, don't know
A language model does not have a fact database that it consults. It generates the most likely answer based on patterns in its training materials.
That explains the strengths and weaknesses at once. Strong in language: summarizing, reformulating, adjusting tone, providing structure. Weak on facts it hasn't seen often, and weak on anything that happened after training.
And it always sounds just as confident. There is no built-in difference between "I know this for sure" and "this seems plausible to me": you have to organize that distinction yourself.
WHY IT MATTERS
What that means for a business agent
This is precisely why we never let an AI agent answer from general knowledge. He gets your documents, prices and systems as a source and answers from them: that's called retrieval, and it's the difference between an agent who is correct and an agent who sounds good.
We build in human approval for actions with consequences. Not because the model is stupid, but because sounding convincing and being right are two different things.
- Answers from our own sources — not from general knowledge of the model.
- Mention source — so that an answer is verifiable.
- Escalation when in doubt — prefer to refer rather than gamble.
- Human approval — in everything with financial or legal consequences.
- Save conversations — you want to be able to look back at what was said.
FREQUENTLY ASKED QUESTIONS
More about an LLM
Why does a language model sometimes make things up?
Because it generates the most likely follow-up instead of looking something up. If the knowledge is lacking, it still produces a fluent response: that's the behavior, not a glitch. That's why we connect agents to real sources.
Which model do you use?
That depends on the task, the desired speed and the costs. More important than the brand is how the agent is built around it: which sources he is allowed to consult, what he is not allowed to do and when he engages a human.
Does the model learn from my company data?
Not with the setup we use. Your data is sent per question as context, not used to train the model. For business API access, this is the default setting of the providers we use.
CONTINUE READING
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