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At the core of most AI tools you’re using (ChatGPT, Claude, Gumloop) there’s a model. This is the engine that processes the text, image, or audio you send and generates a response.

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How Large Language Models (LLMs) Actually Work

AI models are machines that try to 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.
  1. A user provides input such as: “Who was the first president of the United States?”
  2. The model processes the input by mapping it against the vast data from its training and predicts the most likely next word
  3. After selecting a word, the model considers both the original prompt and its generated text to predict the subsequent word
  4. This process repeats iteratively until a complete answer is formed
How models work: predicting the next word
Models iteratively predict the next word until a complete answer is formed.

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From Single Response to Conversation

To go from a single response to a conversation (from a single prompt to a conversation), it’s more of the same process. 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
Models have no persistent memoryWhen 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. In fact, the model doesn’t even have memory between words, it’s starting fresh with every word!

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The Intelligence vs. Speed Tradeoff

With new AI models being released regularly, and even multiple models from the same provider, how should you pick from the different options in Gumloop? Models sit on a spectrum where intelligence and speed are inversely related:
Model intelligence vs speed tradeoff
  • More capable models → Slower responses, fewer mistakes, higher cost
  • Faster models → Quicker responses, more potential for errors, lower cost

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Meet the Model Families

Best ForAnthropicOpenAIGoogle
Complex reasoning, nuanced tasksClaude Opus 4.5GPT-5.2Gemini 3.0
Most business use casesClaude Sonnet 4.5GPT-5Gemini 2.5 Pro
Simple tasks, high volumeClaude HaikuGPT-4.1 MiniGemini 2.5 Flash
Each provider offers models across the spectrum. The naming varies, but the tradeoff is the same: more capable models are slower and cost more, faster models are cheaper but less reliable on complex tasks.

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How to Choose the Right Model in Gumloop

Here’s a recommended strategy for choosing the right model in Gumloop: Start with an advanced model. Begin with a more capable model (like Claude Sonnet or GPT-5.2) to establish a quality baseline. Test your workflow and evaluate the results. Are they good enough? If yes, move down. Try a faster, cheaper model and test again. Keep iterating until you find the perfect balance: the fastest, most affordable model that still delivers quality you’re satisfied with.
When in doubt, start capableIt’s much easier to identify when a simpler model is “good enough” than to debug why your automation is producing mediocre results. Start smart, then optimize for speed and cost.

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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; the model doesn’t remember previous chats
  • 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 to find the best balance for your use case
Now that you understand what models are and how they work, the next question is: how do we give AI access to our tools so it can actually do things for us? That’s what we’ll cover in the next lesson.