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AI engineer interview playbooks
Practical answer frameworks, production tradeoffs, and follow-up angles for real AI engineering interviews. Read them like field notes, not marketing pages.
How do you evaluate a RAG system before shipping it to production?
Open by separating retrieval evaluation from generation evaluation. They fail for different reasons, so I do not score them as one fuzzy quality number. Then I commit to one concrete release path: a frozen golden set…
How would you implement a RAG pipeline from scratch?
The interviewer is not checking whether you can name the libraries. They want to see if you understand where each stage of the pipeline breaks and what production concerns look like before you have even added…
What is Retrieval-Augmented Generation (RAG), and why is it important?
The trap here is giving a buzzword answer about embeddings and vector databases. The interviewer is really testing whether you understand what RAG buys in production: freshness, private knowledge access, attribution,…