What is an LLM?

An LLM works by predicting the most likely continuation of text based on the context it's given. This lets a single model solve many tasks without separate programming for each: it's enough to phrase an instruction (a prompt) and, if needed, provide examples or additional context. Different models differ in quality, speed, cost and context length.

In conversation analysis an LLM handles the "understanding" part of the work: it summarizes a call, determines topics and intents, extracts structured fields from free text, classifies and annotates records by meaning, answers questions about the archive. Where rigid rules used to be needed, an LLM copes with live, unstructured speech.

In voice agents an LLM is the "brain" of the dialogue: it understands the caller's reply, matches it to the scenario and rules and forms a response that is then voiced by speech synthesis. How naturally and accurately the agent holds a conversation depends on the model choice, so different models are picked to fit the task and budget.

It's important to keep the limitations in mind: an LLM can err and "make up" facts if it isn't given reliable context. So in serious scenarios it's grounded on a knowledge base and real data, and deterministic calculations are moved into code where possible, leaving the model precisely the job of understanding language.