Iva P.10 min read
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Contents:
What is a large language model and how does it work?
A high-level summary: How does a large language model work?
Evolution of LLMs: From static models to instruction-following agents
What makes a model “large”?
Generative AI: The broader context
Tokenization: Breaking text into units
Transformer architecture: The backbone of LLMs
Attention mechanism explained simply
Training: How LLMs learn from data
Human feedback and evaluation: How models learn from us
Decoding strategies: How models generate outputs
Inference: What happens when you type a prompt?
Memorization and copyright: What LLMs remember and why it matters
Where LLMs are used today
Risks and limitations of large language models
FAQs
Conclusion: Why understanding LLMs matters
