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LLM Fundamentals — Transformers, Attention & Architecture
Deep understanding of how large language models work — from self-attention and the transformer architecture to modern optimizations (KV cache, Flash Attention, RoPE, GQA). Essential for senior AI/ML engineer interviews.
Why Transformers Dominated
Before transformers (2017), sequence models (RNNs/LSTMs) processed tokens sequentially — impossible to parallelize. Transformers introduced self-attention: every token directly attends to every other token simultaneously. This enabled massive parallelism during training, scaling to billions of parameters with the right hardware.
The transformer architecture (Vaswani et al., "Attention Is All You Need", 2017) is now the backbone of virtually every large AI model: GPT, Claude, LLaMA, Gemini, Whisper, DALL-E.
LLM Training Pipeline — From Raw Text to Deployed Model
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