OverviewWhat it is
Neural networks are layered systems that learn representations from data. The Transformer is the specific architecture behind modern LLMs. Its key idea, self-attention, lets the model weigh how much every token relates to every other - capturing context far better than the sequential RNNs before it, and doing it in parallel so it scales.
At a glanceNeural Networks & the Transformer
Read bottom-up: self-attention lets every token weigh every other token, in parallel.
MechanicsHow it works
Tokens become embeddings; positional encoding adds order; then a stack of blocks (self-attention then a feed-forward layer) refines the representation, ending in next-token probabilities. Transformers come in three shapes: encoder (understanding, e.g. BERT), decoder (generation, e.g. GPT), and encoder-decoder (translation).
Ground levelWhat you actually build
Attention, three shapes, and a bill. Context length and parameter count decide your latency and whether self-hosting is feasible at all.
LandscapeTypes & approaches
Click a highlighted type to open its own page — concept, use case, and diagram.
FeasibilityArchitecture & feasibility
Architecture & feasibility
- Attention cost grows with sequence length, so context window is a direct cost and latency lever - a core architecture-feasibility constraint.
- Encoder models suit search and classification (cheap, fast); decoder models suit generation. Picking the family right avoids over-paying for capability you do not need.
- Model size sets your hardware floor (GPU memory), which sets whether self-hosting is even feasible versus using an API.
In practiceWhat it means for building
You do not need the math, but 'attention = context awareness' explains why LLMs handle nuance and long documents older models could not.
Encoder vs decoder, context length, and parameter count are the levers that set latency, cost, and whether self-hosting is feasible.
GlossaryKey terms
CheckCheck your understanding
Why did Transformers beat RNNs?
Parallel processing (faster training at scale) and self-attention (long-range context without forgetting). RNNs read one step at a time and lose distant context.
Encoder vs decoder?
Encoders build representations for understanding tasks; decoders generate text token-by-token. LLMs are mostly decoders.
Why does context length matter for cost?
Attention scales with sequence length, so longer prompts cost more compute and latency - a real budget constraint.
What changedWhat changed here
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