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 serious problem when the model is asked about recent events, domain-specific information, or anything outside its training scope. Even worse, when such a model lacks the right information, it often “hallucinates”—fabricating answers with complete confidence, much like a know-it-all employee who hasn’t read a new memo in months but still answers every question with assertiveness.

RAG solves this by giving the model access to an external knowledge source, such as a set of documents, a database, or the web, right before it generates a response. Instead of relying solely on its internal memory, the model retrieves relevant information just-in-time and uses that to inform its output. It’s a bit like turning a closed-book exam into an open-book one. The model is no longer guessing based on experience alone—it’s consulting up-to-date resources before responding. This dramatically reduces hallucinations and lets the model handle more precise, timely, or customized questions.

How does RAG work?

A RAG system comprises two main parts: a retriever and a generator. When a user asks a question, the retriever searches a connected knowledge source, like a document corpus or vector database, for the most relevant information. This is usually done using vector embeddings to represent the query and the documents, enabling semantic similarity search.

The top-k relevant documents are then passed to the generator, typically a large language model like GPT. The model is prompted not just with the original question, but also with the retrieved documents. The result is a grounded, context-aware response that blends natural language fluency with fact-based content. Unlike a standalone LLM that must rely entirely on its trained parameters, a RAG model is dynamically informed by the most relevant retrieved knowledge.

This architecture has several advantages: it allows the model to access recent or domain-specific information, reduces hallucinations, and doesn’t require expensive retraining every time the knowledge base changes.

How a RAG system processes a query

To better understand how RAG works in practice, it helps to break down how a query is handled from start to finish.