RAG for LLMs

One of the most critical—and increasingly foundational—questions in modern AI and NLP interviews centers around retrieval-augmented generation (RAG). At first glance, it might seem like a simple question about combining search with language models. But the real test is whether you can reason through why RAG exists in the first place, what fundamental limitations of large language models it addresses, and how retrieval fundamentally changes generation pipelines.

This question isn’t just about being able to define RAG; it’s about demonstrating that you understand the growing need for models that are not only fluent but also factually grounded and dynamically updatable. Interviewers are probing whether you can explain why static knowledge is insufficient, how retrieval pipelines empower LLMs to work with live, domain-specific information, and what new complexities RAG systems introduce compared to traditional extractive QA architectures.

In this breakdown, we’ll review the key aspects an interviewer expects:

• Why retrieval-augmented generation became necessary in modern NLP systems—and the specific problems it solves around static memory, hallucinations, and domain specialization;

• How RAG systems are architected, separating retriever and generator roles to create a just-in-time knowledge grounding mechanism;

• How RAG differs from traditional open-domain question-answering pipelines, and where it introduces new challenges like retrieval dependency, complexity, and imperfect grounding;

By the end, you’ll be ready not just to define RAG, but to explain how it transforms static LLMs into dynamic, adaptable systems—and why mastering this shift is essential for building reliable AI in today’s rapidly evolving information landscape.

What is RAG, and why do we need it?

Imagine an over-enthusiastic employee who hasn’t read any new documents in months but still insists on answering every question as if they’re up to date. Sometimes they’ll be right, but often they’re confidently wrong—and that’s exactly what LLMs do when they guess. RAG fixes this problem by letting the model consult external information before generating its response. Instead of treating the model like a student taking a closed-book exam, RAG gives it access to a knowledge base, like allowing that student to flip through their notes and find the relevant section before answering.

Retrieval-augmented generation (RAG) is a hybrid architecture designed to overcome a major limitation of large language models (LLMs): their static and sometimes unreliable internal knowledge. LLMs are trained on massive but frozen datasets and can only generate responses based on what they’ve seen during training. This becomes a ...