What is an AI Model?
How large language models work under the hood — next-word prediction, conversation context, the intelligence vs. speed tradeoff, and how to choose the right model.
Underneath every AI tool (ChatGPT, Claude, Gumloop) is a model. This is the engine that processes the text, image, or audio you send and generates a response.
How Large Language Models (LLMs) Actually Work
AI models predict the next word in a sequence based on the previous words. They’re called “large” because they’re trained on massive datasets (billions of web pages, books, and articles) and contain billions or even trillions of parameters that help them understand language patterns.
The prediction process works like this:
- A user provides input: “Who was the first president of the United States?”
- The model maps the input against its training data and predicts the most likely next word
- After selecting a word, the model considers both the original prompt and its generated text to predict the next word
- This repeats until a complete answer is formed
From Single Response to Conversation
When you send a follow-up message, the chatbot feeds the entire conversation (every message exchanged so far) back to the model. The model then predicts the next word based on all that context.
When you start a new conversation, the model starts completely fresh. It has no memory of previous chats. Each conversation is independent: the model only knows what’s in the current thread.
The Intelligence vs. Speed Tradeoff
Models sit on a spectrum where intelligence and speed are inversely related:
- More capable models: slower responses, fewer mistakes, higher cost
- Faster models: quicker responses, more potential for errors, lower cost
Each major provider (Anthropic, OpenAI, Google) offers models across this spectrum:
| Use case | Anthropic | OpenAI | |
|---|---|---|---|
| Complex reasoning | Claude Opus | GPT-5.2 | Gemini 3.0 |
| Most business tasks | Claude Sonnet | GPT-5 | Gemini 2.5 Pro |
| Simple, high volume | Claude Haiku | GPT-4.1 Mini | Gemini 2.5 Flash |
How to Choose the Right Model
Start with an advanced model. Begin with a more capable model (like Claude Sonnet or GPT-5) to establish a quality baseline. Test your workflow and evaluate the results.
If the results are good, move down. Try a faster, cheaper model and test again. Keep iterating until you find the fastest, most affordable model that still delivers the quality you need.
It’s much easier to identify when a simpler model is “good enough” than to debug why your automation is producing mediocre results.
Key Takeaways
- Models are next-word predictors: they generate responses by predicting one word at a time based on patterns in their training data
- Chatbots are LLMs with context: they maintain conversations by feeding the entire chat history back to the model
- No persistent memory: each new conversation starts fresh
- Intelligence vs. speed tradeoff: more capable models are slower and costlier, faster models may make more mistakes
- Start advanced, then optimize: begin with a capable model and work your way down