Attention Layer

- Why context is fundamental in LLMs - Limits of n-grams, RNNs, embeddings - Self-attention solves long-range context - QKV: query–key–value mechanics - Dynamic contextual embeddings per token - Attention weights determine word relevance - Multi-head attention = parallel perspectives - GQA reduces attention compute cost - Mixture-of-experts for specialized attention - Editing and modifying transformer layers - Decoder-only vs encoder–decoder framing - Building context-aware prediction systems