The Transformer is the default architecture of modern AI. Its core idea—attention—replaced sequential RNN/LSTM processing and made long‑range reasoning practical and scalable.
1) Why Transformers beat RNN/LSTM
- Parallelism: process all tokens at once instead of step‑by‑step.
- Direct long‑range links: attention connects any two positions without vanishing chains.
- Stable scaling: residuals/layer‑norm/multi‑head make optimization robust at scale.
2) Q/K/V mental model
Each token emits three vectors: Query (what I seek), Key (how findable I am), and Value (what I carry). Relevance ≈ dot(Q, K), softmax → a weighted sum of Values: “who should I listen to, and by how much?”
3) Multi‑Head attention = multiple viewpoints
Different heads learn different relations (syntax, co‑reference, format cues). Concatenating heads yields richer context than a single head.
4) Positional information without recurrence
Attention is order‑agnostic, so we inject positional encodings (sinusoidal or learned) to reason about order and distance.
5) Variants and when to use them
- Encoder‑only (BERT): understanding/retrieval/classification.
- Decoder‑only (GPT family): generation/reasoning/tool use.
- Encoder–Decoder (original Transformer/T5): sequence‑to‑sequence like translation.
6) Long context and efficiency tricks
- Longer context requires careful prompting and stable attention scaling.
- Sparse/linear variants approximate
O(n^2)attention with near‑linear cost. - Retrieval‑augmentation adds external knowledge as focused evidence.
7) Pitfalls to avoid
- “More context” ≠ “better context”: irrelevant text dilutes attention.
- Ignoring positional choices: tasks differ in relative vs absolute needs.
- Assuming “more heads is always better”: consider parameter/VRAM budgets.
8) Practical advice
- Structure inputs: sections, bullet lists, tables/JSON.
- Make constraints explicit: steps, limits, acceptance criteria.
- Few strong examples: 1–2 high‑quality exemplars beat many weak ones.
- Use retrieval: stitch only the most relevant evidence.