What is a large language model (LLM)?
A large language model (LLM) is an AI system trained on huge amounts of text to understand and generate human language. It predicts likely words to answer questions, write, summarise, and reason over text. LLMs are the engine behind AI agents, chatbots, and most modern AI features.
A large language model is the core technology behind today's AI wave. It learns patterns in language from vast text and uses them to generate useful responses, but on its own it only produces text. Its business value comes from what you connect it to.
What LLMs power
- Chat assistants and question answering
- Summarising and drafting documents
- Extracting and classifying information
- The reasoning inside AI agents
LLMs and their limits
An LLM has no memory of your systems and cannot take action by itself. It can also be wrong. That is why real applications ground models in current data and give them tool calling so they act on live systems instead of guessing.
LLMs in Oido
Oido is provider-agnostic: run your agents on the LLM you choose, Claude, GPT, DeepSeek, and others, and switch per agent or task. Open standards like MCP keep your tools working whichever model you pick.
Frequently asked
An LLM generates text. An AI agent wraps an LLM with tools, a goal, and a loop so it can take action and complete tasks. The LLM is the brain; the agent is the brain plus hands and a job to do.
It depends on the task. Models differ in cost, speed, reasoning, and context length, and they change constantly. This is why provider-agnostic platforms let you pick the right model per task rather than committing to one.
LLMs can be confidently wrong, have a knowledge cutoff, and cannot act on their own. Connecting them to live tools and current data, through tool calling and MCP, is how those limits get addressed in practice.