Understanding Transformers Part 5: Queries, Keys, and Similarity
The "Understanding Transformers" series has been running a quiet marathon. Each part lays track that the next part needs. Part 5, arriving on April 11th, reaches the mechanism that earns transformers their reputation: the query-key similarity computation, the moment inside self-attention where the model decides what each token should be paying attention to. The previous installments built toward this — setting up vocabulary, embeddings, the general architecture — knowing that none of it lands without explaining the dot-product comparison at the core. Queries ask. Keys answer. The similarity score between them is what the transformer uses to route information, and getting that intuition right is the difference between a reader who can use the architecture and one who can only repeat its name. The series appears to be targeting the learner who is actually building something, not just nodding along at a conference.
If confirmed as part of a sustained, complete series, what this means is simpler than it sounds: there is a compounding shortage of technical writing that treats the reader as an adult without abandoning them mid-abstraction. Most transformer explainers either stop at the diagram or collapse into pure mathematics. A series that reaches Part 5 with its pedagogical spine intact is serving the growing cohort of developers who are vibe-coding adjacent — building with AI tools, starting to interrogate why those tools behave as they do, and finding the official literature impenetrable. That audience is large, growing, and underserved. Second-order effect: when foundational concepts become genuinely accessible, the ceiling on who can contribute to AI tooling rises. That matters more than any single explainer.
Watch for Parts 6 and beyond — whether they arrive on a consistent schedule and whether the series completes the attention mechanism through softmax and value weighting, which would confirm this is a finished curriculum rather than a series that stalled at the hard part.
<p>In the <a href="https://dev.to/rijultp/understanding-seq2seq-neural-networks-part-8-when-does-the-decoder-stop-4jdi">previous articles</a>, we understood Seq2Seq models. Now,
<p>Back in 2017, the first transformer architecture introduced two main components:</p> <ul> <li>an <strong>encoder</strong> </li> <li>a <strong>decoder</strong> </li> </ul>
<p>In this article, we will look at the differences between a <strong>decoder-only transformer</strong> and a <strong>standard (encoder–decoder) transformer</strong>.</p> <h2> How
<p>In the <a href="https://dev.to/rijultp/understanding-transformers-part-15-scaling-and-combining-values-in-encoder-decoder-attention-4dfm">previous article</a>, we handled
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