AI Engineering Interviews Now Focus on RAG Pitfalls, Evaluation and System Reliability
As AI engineering roles grow more competitive, interviewers are shifting focus from tool familiarity to deeper judgment about what makes retrieval-augmented systems fail. Common problem areas include chunking strategy, hybrid search for exact matches, stale indexes, and confidently retrieving irrelevant content. A key differentiator between junior and senior candidates is the ability to diagnose whether failures stem from retrieval or generation, since each requires a different fix. Strong candidates also demonstrate structured evaluation practices — building fixed test sets from real failures and continuously feeding production errors back as regression cases. The ability to validate model outputs against a schema and maintain reliable evaluation pipelines is increasingly what separates those who have shipped production AI systems from those who have only prototyped them.
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