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What is an LLM? - Definition & Meaning

Learn what a Large Language Model (LLM) is, how ChatGPT and other models work, and why LLMs form the foundation for AI agents and chatbots.

An LLM (Large Language Model) is an AI model trained on vast amounts of text to understand and generate language. Examples include ChatGPT, Claude, and Gemini. LLMs can produce text, answer questions, and generate code.

What is What is an LLM? - Definition & Meaning?

An LLM (Large Language Model) is an AI model trained on vast amounts of text to understand and generate language. Examples include ChatGPT, Claude, and Gemini. LLMs can produce text, answer questions, and generate code.

How does What is an LLM? - Definition & Meaning work technically?

LLMs are transformer networks (GPT architecture). Pre-trained on web text, fine-tuned for tasks. Concepts: tokens, context window, RAG. APIs: OpenAI, Anthropic, Google.

How does MG Software apply What is an LLM? - Definition & Meaning in practice?

MG Software integrates LLMs into client projects for chatbots, summaries, and code assistance. We use RAG to combine domain knowledge with LLM capabilities and build AI agents for automated workflows.

What are some examples of What is an LLM? - Definition & Meaning?

  • A customer service chatbot answering questions with RAG-based knowledge from the knowledge base.
  • A tool that automatically summarizes documents and extracts key points for faster decision-making.
  • A code assistant helping developers with boilerplate and refactoring suggestions.

Related terms

ai agentsmachine learningapisaas

Further reading

Knowledge BaseWhat is Deep Learning? - Definition & MeaningWhat is Speech-to-Text? - Definition & MeaningAI Automation Examples - Smart Solutions with Artificial IntelligenceChatbot Implementation Examples - Inspiration & Best Practices

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Frequently asked questions

An LLM is the underlying model; a chatbot is the application that uses the LLM through an interface. A chatbot can combine multiple LLMs or other models.
Retrieval Augmented Generation: first you retrieve relevant information from a knowledge base, then you pass it to the LLM. The model answers with that context, reducing hallucinations.
With the right architecture, yes: use RAG for factual knowledge, validate outputs, and have fallbacks for failures. We use LLMs as support, not as the sole source of truth.

What is the difference between an LLM and a chatbot?

An LLM is the underlying model; a chatbot is the application that uses the LLM through an interface. A chatbot can combine multiple LLMs or other models.

What is RAG?

Retrieval Augmented Generation: first you retrieve relevant information from a knowledge base, then you pass it to the LLM. The model answers with that context, reducing hallucinations.

Are LLMs suitable for business-critical systems?

With the right architecture, yes: use RAG for factual knowledge, validate outputs, and have fallbacks for failures. We use LLMs as support, not as the sole source of truth.

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