LLM

Simple Definition

LLM stands for Large Language Model. It’s the type of AI technology that powers tools like ChatGPT, Claude, and Gemini.

In plain terms: an LLM is a very large AI system trained on enormous amounts of text. After training, it can generate text, answer questions, write code, summarize documents, and perform many other language tasks.

Why “Large”?

LLMs are “large” in two senses:

1. Training data — they’re trained on massive amounts of text from the internet, books, code, and other sources
2. Parameters — they have billions or trillions of internal numerical values that encode what they’ve learned

The scale is what makes them capable of handling such a wide range of tasks.

How LLMs Work (Simply)

An LLM is trained to predict the next word in a sequence. After being trained on enough data, this ability generalizes into something much more powerful — the model can write essays, answer questions, debug code, and reason through problems.

The model doesn’t “know” things the way humans do. It has learned statistical patterns across massive amounts of text, and it generates responses based on those patterns.

LLMs You’ve Probably Used

• GPT-4 / GPT-4o — the model behind ChatGPT
• Claude 3 / Claude 4 — Anthropic’s models
• Gemini — Google’s LLM family
• Llama — Meta’s open-source LLM

Limitations of LLMs

• They can generate confident-sounding incorrect information (“hallucinations”)
• They have a knowledge cutoff date — they don’t know about recent events unless connected to search
• They don’t truly “understand” — they pattern-match on learned data
• They can be inconsistent — the same prompt may produce different outputs

Related Terms

• Context Window — how much text an LLM can process at once
• Prompt Engineering — writing instructions that get better outputs from LLMs
• RAG — a technique for connecting LLMs to external data