Definition
A large language model (LLM) is an AI model trained on very large amounts of text to understand and generate human language and to follow instructions. It is the reasoning engine behind modern chatbots, copilots, and AI agents - for example Claude, GPT, and Gemini.
What an LLM does
An LLM takes text as input and predicts useful text as output. That simple mechanism, trained at scale, lets it summarize documents, answer questions, write and review code, extract structured data from messy text, and follow multi-step instructions. It is the component that gives an AI agent its ability to reason about a task.
What an LLM is not
An LLM on its own has no memory of your business, no access to your systems, and no ability to take action. It is a capable reasoning engine, not a complete solution. Turning an LLM into something useful for operations means connecting it to your data (often via retrieval-augmented generation) and giving it tools to act - that surrounding engineering is where most of the real work lives.
Frequently Asked Questions
What is the difference between an LLM and an AI agent?
An LLM is the reasoning engine. An AI agent wraps an LLM with memory, access to your systems, tools it can call, and a goal - so it can complete a job across multiple steps rather than just answer a single prompt.
Which large language model should a business use?
It depends on the task, cost, and data-handling requirements. For most business applications, the current top-tier models from major providers (such as Claude, GPT, and Gemini) are all capable; the right choice is driven by your specific accuracy, latency, and privacy needs.
Can an LLM use my company's private data?
Not by default - it only knows its training data. To use your private data safely, you connect it at runtime through techniques like retrieval-augmented generation (RAG), which lets the model reference your documents without being retrained on them.
Put this into practice
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