Transformers for AI Engineers
Attention, KV caching and what parameter count really buys you — the internals an AI Engineer interview expects you to reason about, not just name
Covers what an AI Engineer interview actually probes about transformer internals: scaled dot-product and multi-head attention, why decoder-only architectures won, why the KV cache exists and how its memory footprint is computed, what parameter count does and doesn't predict, and positional encoding — with worked numbers for KV cache memory and prefill-vs-decode cost.
Practice questions (5)
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Explaining Why Long Context Costs More Than It Looks Like It Should
Intermediate · Free -
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Choosing an Architecture Shape for a New Product Feature
Intermediate -
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Pushing Back on 'Just Use the Bigger Model'
Intermediate -
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Evaluating a Proposed Attention Change for Cost Reasons
Intermediate -
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Diagnosing Quality Loss Past the Trained Context Length
Intermediate