Glossary
What is a large language model?
Definition
A large language model (LLM) is a type of AI model trained on vast amounts of text to predict and generate human-like language. It learns statistical patterns in words and can answer questions, summarize, translate, and hold conversations. LLMs power many modern chatbots and voice assistants by generating context-aware responses.
01How LLMs work
LLMs are typically built on the transformer architecture, which uses attention mechanisms to weigh the relationships between words in a sequence. During training they learn to predict the next token given prior context, absorbing grammar, facts, and reasoning patterns from their data. At runtime they generate text one token at a time, conditioned on the prompt and prior output.
02LLMs in voice and phone systems
In a voice application, an LLM often sits between speech recognition and speech synthesis, interpreting the transcribed request and composing a reply. It can track conversation context, follow instructions, and decide when to call external tools such as a calendar or CRM. Careful prompting and guardrails help keep responses accurate and on-topic for a specific business.
03Strengths and limitations
LLMs are flexible and handle open-ended language well, but they can produce fluent yet incorrect statements, sometimes called hallucinations. Their knowledge is bounded by training data and can become outdated, so systems often supplement them with retrieval of current information. Response quality depends on prompt design, grounding data, and safeguards against off-topic or unsafe output.
Frequently asked questions
What does the 'large' in large language model mean?
It refers to the model's scale, including the number of parameters it contains and the volume of text it was trained on, which is far larger than earlier language models.
Do LLMs always tell the truth?
No. LLMs can generate plausible-sounding but incorrect information. Reliable systems ground them in verified data and add checks to reduce errors.
See also
Related terms
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