Introduction to Building an LLM

- Intuition for decoder-only LLMs - Tokens, embeddings, transformer pipeline - Autoregressive next-token generation - Generative AI modalities overview - Diffusion vs transformer model families - Inference flow and prompt processing - Build a real LLM inference API - Architecture: attention, context, decoding - Training phases: pretrain to RLHF - Vertical vs generic LLM design - Distillation, quantization, efficient scaling - Reasoning models: Chain of Thought and Test Time Compute - Hands on Exercises