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Unpacking DeepSeek-R1's Multi-head Latent Attention (MLA), DeepSeekMoE 210B sparse routing, and group relative policy optimization (GRPO) reinforcement learning.
A RAG demo answering questions correctly over ten hand-picked documents proves almost nothing about how it behaves over a real corpus. Seven concrete ways retrieval-augmented generation breaks in production, and what actually fixes each one.
Senior engineers walk into this question and either downplay a real failure into something trivial, or overshare something career-damaging. Neither answers what the interviewer is actually evaluating — and the part of the story most candidates skip is the part that matters most.
Rate limiting looks like a one-line INCR in a whiteboard interview and turns into a race-condition minefield the moment it's distributed across nodes. A tour of the four real algorithms, and the atomicity bug that ships in more Redis rate limiters than anyone admits.
HPA, VPA, and KEDA solve three different scaling problems, and running the wrong one is why 'autoscaling doesn't work' tickets keep coming back. A field guide to picking the right one — and why HPA and VPA fight each other when misconfigured together.
How Grouped-Query Attention (GQA) and Multi-Query Attention (MQA) reduce KV-cache memory bandwidth consumption in LLM inference.
Avoid repeating $0.03 LLM queries by matching semantically similar user prompts in vector cache before hitting API endpoints.
Moving beyond naive vector lookup: building adaptive multi-agent retrieval routers that decide when to query vector databases vs SQL databases.
DPO eliminates the need for training separate reward models and complex PPO reinforcement loops by deriving the implicit reward directly from binary preference data.
How malicious instructions hidden inside scraped web pages or user uploaded PDFs can hijack your LLM agent's execution flow.
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