LLM inference splits into compute-heavy prefill and memory-bandwidth-sensitive decode. Learn why batching helps, how long prompts disrupt streaming, and which latency metrics to measure.
AI
52 posts
In a vector database system design interview, focus on chunk policy, metadata, filtered search, and rebuild strategy—not just the store you name.
Learn how to design a RAG system for interviews by separating retrieval from generation, with distinct freshness, latency, failure, and quality decisions.
When an AI assistant can write working code in seconds, interviewers stop testing typing speed and start testing whether you can clarify requirements, own the design, and verify what the model produced.
When interviewers ask you to design an AI-powered feature, they're testing system design judgment, not ML knowledge — this post shows how to treat the model as an unreliable dependency and reason about its latency, cost, and failure modes.
AI can speed up system design interview prep and create a false sense of readiness. Here's how to use it to sharpen your reasoning while keeping the thinking yours.
A practical guide to using AI for software engineering interview prep, from behavioral answers to system design, without losing the details that make you credible.
Learn how to explain AI project impact in interviews with stronger metrics, user outcomes, and before-and-after results.
Learn how to scope a personal AI project that shows problem-solving, product thinking, and technical judgment.
