Glossary · AI & Agents
Large Language Model (LLM)
Overview
How large language models work
An LLM is a neural network trained on huge volumes of text to predict the most likely next token—a word or word-piece—given everything before it. Repeating this prediction generates fluent sentences. During training it absorbs grammar, facts, and patterns of reasoning, encoded as billions of numerical parameters.
At use time you send a prompt and the model responds token by token. It has no live database inside it; it draws on patterns learned during training, which is why its knowledge has a cutoff date and why techniques like RAG are used to supply current, specific facts.
What LLMs are good at—and their limits
LLMs excel at language tasks: drafting and summarizing, answering questions, translating, extracting structured data, writing code, and classifying text. Their flexibility is what makes them general-purpose. Given a clear instruction, one model can handle many jobs that used to need separate systems.
The main limits are hallucination (confidently stating wrong facts), sensitivity to how prompts are worded, no built-in access to private or real-time data, and cost and latency at scale. Production systems address these with prompt engineering, retrieval, guardrails, and evaluation.
How we put LLMs to work
In AI consulting and agent projects, we help businesses choose the right model and wire it into real workflows—grounding it in your data with RAG, constraining it with guardrails, and measuring output with evaluations. The goal is a system that's accurate and reliable for your specific use case, not just an impressive demo.
Where we use it
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How our team puts Large Language Model to work in real projects.
FAQ
Large Language Model — common questions
What's the difference between an LLM and ChatGPT?
An LLM is the underlying model; ChatGPT is a product built on top of one. GPT is the model family, and ChatGPT is the chat application that uses it, adding an interface, safety layers, and tools.
Which large language model is best?
There's no single best. GPT, Claude, and Gemini each lead on different tasks, cost points, and context sizes, so the right choice depends on your use case, budget, and privacy needs. Many systems route different tasks to different models.
Can an LLM use my company's data?
Not on its own—its knowledge is limited to training data with a cutoff. To use your data you connect it via retrieval-augmented generation, tools, or fine-tuning, which lets the model answer from your documents while keeping that data under your control.
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