Hi everyone,
I hope you’re all doing well! I’m currently preparing for an upcoming interview where the focus will be on RAG (Retrieval-Augmented Generation) and its applications, especially in enterprise-level environments. As someone who’s relatively new to combining RAG with MDM or endpoint management solutions like Hexnode, I’m trying to get a better grip on both theoretical and practical aspects of the topic.
I wanted to ask if anyone here has come across RAG interview questions recently or has been through an interview where RAG was discussed in detail? I’m particularly looking for questions that not only cover the basic architecture of RAG (retriever + generator) but also how it can be applied in real-world use cases—maybe something related to documentation automation, intelligent ticketing systems, or integrating AI with device management workflows.
Some specific areas I’m trying to understand better:
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Common RAG-related questions asked during technical interviews.
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How RAG differs from traditional QA pipelines or vector-based search.
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Potential integration points of RAG in MDM platforms (like how Hexnode might utilize it for contextual support or query resolution).
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Any performance or scalability-related concerns that interviewers tend to dig into.
If anyone has resources, mock questions, or personal experiences they’d be willing to share, I’d really appreciate it! Even just a few sample questions or talking points would help a lot.
Thanks so much in advance!
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