🤖 How AI & ChatGPT Work
How LLMs like ChatGPT train, tokenize, and respond
What you’ll learn
- Breaking Down Text into TokensUnderstand how text becomes tokens the model can processTokens turn your words into numbers the model reads so length and vocabulary directly shape every interaction.
- Training Large Language ModelsLearn the core ideas behind how LLMs are trainedTrillions of words and gradient updates shape billions of weights until the model predicts text well.
- Predicting the Next TokenSee how probability and sampling create responsesAt every step the model calculates probabilities then samples a token within its context window.
- Attention and ContextGrasp how attention mechanisms create contextual understandingAttention layers weigh relationships across tokens letting the model focus on what matters most right now.
- Crafting Effective PromptsApply prompting techniques that improve output qualityClear roles, examples, and step-by-step requests guide the model toward the results you want.
Questions this course answers
What is a token in an LLM?
Tokens are the basic units the model processes; common words stay whole while rarer ones split so the model can handle any text.
What does the training process mainly adjust?
Training repeatedly tweaks the model's weights so it becomes better at predicting the next token on new text.
What happens when temperature is raised?
Higher temperature increases the chance of selecting less probable tokens which produces more varied and creative output.
What does attention allow the model to do?
Attention mechanisms let every token influence the current prediction according to learned relevance patterns.
Why does chain-of-thought prompting help?
Asking for step-by-step reasoning guides the model to produce more accurate final answers by building an explicit path.
Grounded in trusted sources
- Massachusetts Institute of Technology
- Stanford University
- Carnegie Mellon University
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