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LLM (Large Language Model)
What is an LLM (Large Language Model)?
A large language model is an AI model trained on enormous amounts of text to predict the next word in a sequence. From that single, simple task it learns enough about language to answer questions, summarize documents, write code, and hold a conversation. Models like GPT, Claude, and Gemini are LLMs. They are a kind of neural network, specifically a transformer, scaled up to billions of parameters.
An LLM does not look anything up or reason the way a person does. It generates text one token at a time based on patterns learned during training, which is why it can sound fluent and confident while being wrong. That failure mode is called hallucination. Because the model only knows what was in its training data, teams often pair it with retrieval, feeding the model relevant documents at query time so it answers from real sources rather than memory. A support assistant that pulls answers from a company's own help center before replying is using this pattern, known as retrieval-augmented generation.
LLMs power most of the recent wave of chatbots and virtual assistants. The model is the engine, but a useful product wraps it in guardrails, real data, and a clear sense of what it should and should not try to answer.
LLMs at Dallonses
We build with LLMs where they earn their place, and we're direct with clients about where they don't. A model that hallucinates a refund policy is worse than no chatbot at all, so most of our work goes into grounding the model in real data and constraining what it's allowed to say. The interesting engineering is rarely the prompt.
Our LLM work often shows up as chatbots and virtual assistants that answer from a client's actual knowledge, not from whatever the model half-remembers. We wire in retrieval, evaluate the answers against real questions, and put limits in place so the system fails safely. Done well, these AI solutions handle the repetitive questions and hand the genuinely hard ones to a human, which is the line we design toward.
Thinking about putting an LLM in front of your customers? Let's do it safely.
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