This is a guide to how the large language models behind our voice AI agents actually work, and how we use them at AI Squad for smarter customer interactions.
First, one thing worth stating plainly: LLMs are not people, and they do not "understand" the way we do. They are mathematical models working on probability. Remarkably effective pattern recognition, and nothing more.
What an LLM is
A large language model is a machine system trained on an enormous amount of text. It has learned how people use language. It can continue a sentence, answer a question, and hold a conversation — but it does not think, it predicts.
Not "intelligence" — statistical pattern recognition at scale.
How it works, in three steps
1. From words to numbers — embedding
The model does not understand language in the ordinary sense. It turns words into numbers: every word becomes a vector in several hundred dimensions. In that mathematical space, "invoice" sits close to "transaction" and "balance", because they appeared in similar contexts in the training data.
2. From numbers to decisions — the transformer
The transformer is the mechanism that pulls out the meaningful relationships between words, based on context. It does not only look at the previous word but at what follows too — and uses the context of the whole sentence, and of the whole text. In the end it calculates which word is the most likely next one.
3. From numbers back to words — generating the answer
The model picks the most probable word and turns it back into text. It repeats that until the full answer exists. Every single word choice is a function of all the words before it.
Where meaning comes from
Think of it as a map. Every word is a point in a multi-dimensional space, and words with similar meanings sit close together. Here is the interesting part: the two senses of "bank" (the financial institution versus the side of a river) land in entirely different positions, depending on context. The model has learned that the same word form means different things in different surroundings.
What a prompt is
A prompt is a compass: it shows the model where to look for the answer. It activates certain zones in that multi-dimensional space where the knowledge sits. Which is exactly why wording matters so much — a well-written prompt gets a substantially better result than a vague question.
Our call center guide goes into how we use prompts to steer customer service AI agents.
Fine-tuning versus prompting
- Prompt: like repainting the car — visible, but not permanent. It does not change how the model works, only what happens in that conversation.
- Fine-tuning: like fitting a turbo — a new level of performance, for the long run. It changes the model's weights, and the behavior changes with it.
In practice, for most business applications — including our sales and helpdesk solutions — a well-written prompt and the right system configuration are enough. Fine-tuning becomes necessary when the model has to learn an entirely new domain or a specialized behavior pattern.
Hallucinations — when the model is confidently wrong
A hallucination is the model stating something untrue with confidence. It is not "lying" — it works from patterns. If something looks statistically likely, it picks it, whether or not the statement matches reality.
Which is exactly why the defense layers and careful data handling matter: AI running in a business setting has to be reliable, and that has to be backed by technical guarantees.
The key terms
- Token: the smallest unit of text the model processes (a word, a syllable, or a punctuation mark).
- Embedding: turning words into numbers in a multi-dimensional space — how the model "gets at" meaning.
- Transformer: the architecture that analyzes context and surfaces the meaningful relationships.
- Prompt: the instruction that points the model in a direction.
- Fine-tuning: changing the model's weights with specialized training data — a lasting change in behavior.
- Hallucination: a statistically likely but untrue statement from the model.
In short
LLMs are not magicians and they are not thinking entities. What they know, they know statistically. They do not think, they calculate. That operating principle is both their limit and their strength.
AI does not replace people, but it can be a reliable and effective partner — if you teach it well and handle it well. That is what we build on at AI Squad: knowing the technology precisely, respecting its limits, and getting the most business value out of it.
If you want to understand how these models could apply in your business, get in touch or book a call.