Author(s): Rajesh Kumar

Originally published on Towards AI.

A beginner-friendly explanation of attention, Q/K/V, decoder-only models, and the architecture behind modern LLMs

A Transformer is a neural network architecture designed to model relationships between elements in a sequence.

After introducing transformers as models of relationships in sequences, the article explains why language processing is hard (meaning depends on token order and relationships), how RNNs provided limited “memory” but suffered from long-range dependency issues and sequential training bottlenecks, and how attention addresses these by letting tokens directly look at other relevant tokens. It then demystifies the mechanics of attention using Query/Key/Value, discusses how contextual meaning changes through self-attention, and shows how positional information is still required for order. The author covers multi-head attention and what happens inside a transformer layer (self-attention plus feed-forward steps, with residual connections and normalization), distinguishes the original encoder-decoder transformer from GPT-style decoder-only models, and explains autoregressive generation with causal masking—leading to how responses like ChatGPT produce text token-by-token. Finally, it outlines why transformers became so effective (parallel training, scalability, and flexibility across modalities), notes remaining limits such as quadratic attention cost, and emphasizes a key boundary: attention improves how information influences outputs, but it does not guarantee factual correctness without retrieval, tools, or other system components.

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Published via Towards AI