AI Engineers: The 7 Key Challenges of RAG Systems in Interviews

Introduction

In the ever-evolving field of artificial intelligence, interviews for engineering positions are transforming. The focus is now on the ability to design complex systems rather than just understanding the basics of RAG, or retrieval-augmented generation. This article explores the seven design questions that candidates frequently encounter during these interviews.

Design Questions

• Designing an end-to-end RAG system: The first key question concerns the creation of a high-performing RAG system. Candidates must demonstrate their ability to design a system that effectively integrates data retrieval and generation while rigorously evaluating its performance.

• Differentiating between RAG and corrective/agentic RAG: Another common question revolves around the distinction between traditional RAG systems and those that incorporate corrective or agentic elements. Engineers need to know when and why to use each type of system, understanding their fundamental differences.

• Designing an agent with real actions: Interviews also explore the design of agents capable of executing concrete actions. Candidates must show how they can create agents that adhere to strict safety rules while operating outside the language model.

• Logic in the orchestrator vs LLM: A crucial technical question concerns the distribution of logic between the orchestrator and the language model (LLM). Engineers must decide which logics should be integrated into the orchestrator and which should remain within the model.

• Debugging hallucinations or loops: A major challenge is knowing how to isolate retrieval failures from live generation failures. Candidates must propose solutions to address these complex issues.

• Controlling costs and latency at scale: Managing costs and latency is critical in large-scale systems. Engineers must master strategies such as batching, caching, routing, and context trimming to avoid unnecessary overhead, especially in multi-agent environments.

• Evaluating RAG and agents before and after deployment: Finally, evaluating systems before and after their deployment is essential. Candidates must know how to separate retrieval and generation metrics to define the success and efficiency of agents.

Common Mistakes

The article also highlights common mistakes that candidates should avoid. These include the tendency to remain too abstract in their responses, correcting architectural issues through prompt adjustments, and recommending overly complex configurations. Neglecting failure modes is also a frequent error.

Practical Preparation

To prepare effectively for interviews, candidates are advised to practice answering design questions with a depth appropriate to their level of seniority. Preparation should be rigorous to meet the increasing expectations of recruiters in this rapidly changing field.