What is Large Language Model (LLM)?
A large language model (LLM) is a neural network trained on very large text collections to predict the next token, which lets it understand and generate human language for tasks like answering, summarizing and drafting.
How It Works
An LLM learns statistical patterns in language by training on very large text corpora, repeatedly predicting the next token. That single skill lets it summarize, classify, extract data, translate and hold a conversation. Well-known examples are Claude from Anthropic, GPT from OpenAI and Gemini from Google. LLMs have real limits: a context window caps how much text they can read at once, they can hallucinate answers that sound right but are wrong, and every call costs money per token. For example, an automation can send an incoming support email to an LLM and get back a category and a draft reply, with a person reviewing anything uncertain.
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Key Benefits
- Understands and generates natural language
- One model handles many tasks, from summarizing to extraction
- Turns unstructured text into structured data
- Reachable through an API from any workflow
- Pairs with retrieval to answer from your own documents
Common Use Cases
- Summarizing and classifying incoming emails
- Extracting fields from contracts and invoices
- Drafting replies for human review
- Powering the reasoning step inside an AI agent
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Related Terms
Workflow Automation
The use of technology to automate repeatable business processes, reducing manual intervention and ensuring tasks are completed consistently and efficiently.
AI Agents
Autonomous software programs powered by artificial intelligence that can perceive their environment, make decisions, and take actions to accomplish specific goals.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique where a system first retrieves relevant passages from your own documents, then has a large language model answer using only those passages, so responses stay grounded in your data.
Model Context Protocol (MCP)
Model Context Protocol (MCP) is an open standard, introduced by Anthropic in November 2024, that lets AI applications connect to tools and data sources through one common protocol instead of custom integrations.
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