A structured framework for AI engineer system design interviews, covering end-to-end ML system design, data pipelines, model serving, monitoring, and A/B testing.
A structured framework for answering LLM fine-tuning interview questions, covering LoRA, QLoRA, full fine-tuning, data preparation, evaluation, and when to fine-tune versus prompt.
A structured framework for answering prompt engineering interview questions, covering few-shot prompting, chain-of-thought, system prompts, temperature tuning, and structured output.
A structured framework for answering transformer architecture interview questions, covering self-attention, multi-head attention, positional encoding, KV cache, and Flash Attention.
A structured framework for answering vector database comparison questions in AI engineering interviews, covering Pinecone, Weaviate, Qdrant, Chroma, Milvus, managed vs. self-hosted tradeoffs, and cost analysis.