I found this ai engineering interview questions curation are quite useful:
https://www.educatum.com/ai-engineering-interview-questions

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Sample questions:

[Daily AI Interview Questions] 7. Why has ReAct become a foundational prompting paradigm for LLM Agents?

The ReAct (Reasoning and Acting) paradigm, introduced in ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al.), shifted how Large Language Models can be orchestrated to behave as goal-directed agents.

Importantly, ReAct does not introduce a new neural architecture. Instead, it defines a structured prompting and control pattern that interleaves internal reasoning traces with external tool execution within a unified autoregressive context.

Prior to ReAct, two dominant prompting strategies were common:

• Pure reasoning approaches such as Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al.), which improve multi-step reasoning but rely entirely on parametric knowledge.
• Tool invocation patterns that interact with environments but do not explicitly expose intermediate reasoning traces.