Learning path · Transformers & Attention · 16
Transformers
Neural architecture using self-attention to relate all tokens in a sequence—foundation of modern LLMs and many multimodal models.
Why it matters
- Explains why context length and attention cost dominate design discussions.
- Clarifies encoder-only vs decoder-only roles in retrieval stacks.
- Informs when separate embedding models beat generative models.
Key ideas
- Self-attention
- Parallel sequence processing
- Layer stacks
Transformers replaced recurrent networks by letting every token attend to every other token in a layer—capturing long-range dependencies with trainable parallelism. Decoder-only stacks power autoregressive LLMs; encoder stacks produce dense vectors for search. Understanding transformers at a systems level helps you reason about latency (quadratic attention in full attention), memory, and why approximate attention and sliding windows appear in long-context models. You do not need to implement attention by hand, but you should know why doubling context length increases serving cost and memory pressure.
Updated 2026-08-09 · Full learning path