9. Components of a RAG System

A production RAG system usually includes:

Data Source

Document Loader

Text Splitter

Embedding Model

Vector Database

Retriever

LLM

Response Generator

Each component plays a role in retrieving and generating accurate responses.

10. Advantages of RAG

Reduces hallucinations

Uses the latest information

Supports private enterprise data

No need to retrain the model frequently

Lower cost than fine-tuning for changing knowledge

Improves response accuracy

11. Challenges in RAG

Poor document chunking

Low-quality embeddings

Irrelevant retrieval results

Slow retrieval

Large context windows

Duplicate information

Outdated documents

Optimizing retrieval quality is often as important as choosing the right LLM.

12. RAG vs Fine-Tuning

RAG:

Retrieves external knowledge

Best for frequently changing data

No model retraining

Easier to update knowledge

Reduces hallucinations with grounded context

Fine-Tuning:

Updates model behavior

Best for specialized tasks

Requires additional training

More expensive to maintain

Improves task-specific performance

Rule of Thumb:

Use RAG when knowledge changes frequently.

Use Fine-Tuning when you need the model to adopt a specific style, behavior, or domain expertise.

13. Common Interview Questions

What are embeddings?

Why are embeddings important?

What is a vector database?

What is semantic search?

How does similarity search work?

What is RAG?

Explain the RAG architecture.

What are the components of a RAG pipeline?

What are the challenges in RAG?

RAG vs Fine-Tuning?

🎯 Interview Tip

When explaining RAG, use this simple flow:

Documents

↓

Chunking

↓

Embeddings

↓

Vector Database

↓

Retriever

↓

LLM

↓

Final Response

This end-to-end pipeline is one of the most frequently discussed architectures in GenAI interviews and demonstrates a strong understanding of enterprise AI systems.

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