LLM Transformer Architecture & Systematic Prompt Engineering
Understand Transformer self-attention, BPE tokenization, temperature sampling, and zero-shot/few-shot/chain-of-thought prompting.
Learning Objectives & Transformer Mechanics
### Learning Objectives
- Understand Transformer self-attention mechanisms and auto-regressive next-token prediction.
- Master prompt engineering techniques: System Instructions, Few-Shot Demonstrations, and Chain-of-Thought (CoT) reasoning.
- Control generation randomness using `temperature`, `top_p`, and frequency penalties.
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### How Large Language Models Work
Large Language Models (LLMs) are auto-regressive deep neural networks trained on massive textual corpora. Text inputs are split into sub-word units called **Tokens** using Byte-Pair Encoding (BPE). The **Transformer Self-Attention** layer computes contextual relationships between tokens, calculating probability distributions to generate subsequent tokens.
