RAG Architecture Interview Questions: Everything You Need to Know

Understanding RAG: The Foundation

Retrieval-Augmented Generation (RAG) has become the go-to pattern for building production AI applications. If you're interviewing for AI engineering roles, RAG knowledge is often mandatory.

What is RAG?

Definition: A technique that enhances LLM responses by retrieving relevant information from external knowledge bases and including it in the prompt context.

Why RAG Matters:

• Reduces hallucinations by grounding responses in facts
• Enables use of private/proprietary data
• Keeps information current without retraining
• More cost-effective than fine-tuning for many use cases

Core Components Questions

Q: Walk me through the RAG pipeline.

Strong Answer:

1. **Document Ingestion**: Load documents, parse various formats
2. **Chunking**: Split documents into appropriately sized pieces
3. **Embedding**: Convert chunks to vector representations
4. **Indexing**: Store vectors in a vector database
5. **Query Processing**: Convert user query to embedding
6. **Retrieval**: Find most similar chunks using vector similarity
7. **Context Assembly**: Combine retrieved chunks with query
8. **Generation**: LLM produces response based on context
9. **Post-processing**: Optional filtering, formatting, citations

Q: How do you choose chunk size and overlap?

Strong Answer: "It depends on the content type and use case. For technical documentation, I typically use 500-1000 tokens with 50-100 token overlap. Smaller chunks provide more precise retrieval but may lose context. Larger chunks preserve context but may include irrelevant information. Overlap ensures important information at boundaries isn't lost. I'd run experiments comparing retrieval quality at different sizes for the specific corpus."

Q: Compare different embedding models.

Cover:

• **OpenAI text-embedding-3**: Strong general-purpose, easy to use
• **Cohere embed**: Good multilingual support
• **E5/BGE**: Open-source alternatives, can self-host
• **Domain-specific models**: Better for specialized content

Key considerations: dimensionality, performance on your domain, cost, latency, and self-hosting requirements.

Vector Database Questions

Q: Compare Pinecone, Weaviate, Chroma, and Pgvector.

Pinecone: Managed service, scales well, easy to use, can be expensive

Weaviate: Feature-rich, hybrid search built-in, can self-host

Chroma: Simple, good for prototyping, embedded option

Pgvector: If you already use Postgres, avoids new infrastructure

Q: What is hybrid search and when would you use it?

Strong Answer: "Hybrid search combines vector similarity search with traditional keyword search (BM25). It's useful when you need both semantic understanding and exact keyword matching - like when users search for specific product codes, error messages, or proper nouns that embedding models might not handle well. Most production systems benefit from hybrid search, typically weighted 0.7 semantic + 0.3 keyword."

Advanced RAG Patterns

Q: What is query expansion/transformation?

Strong Answer: "Query transformation techniques improve retrieval by modifying the user's query. Examples include:

• **HyDE**: Generate a hypothetical document that would answer the query, then use its embedding for retrieval
• **Query expansion**: Add synonyms or related terms
• **Query decomposition**: Break complex queries into sub-queries
• **Step-back prompting**: Ask a more general question first"

Q: Explain reranking in RAG.

Strong Answer: "Initial vector retrieval is fast but approximate. Reranking takes the top-k results (say, 20) and uses a more expensive model (cross-encoder) to re-score and re-order them. You then take the top-n (say, 5) for final context. This improves relevance significantly, especially for complex queries. Popular rerankers include Cohere Rerank and cross-encoder models from Hugging Face."

Q: How do you handle multi-document reasoning?

Key approaches:

• Retrieve from multiple sources, synthesize in prompt
• Iterative retrieval: answer leads to new queries
• Map-reduce: summarize each document, then combine
• Knowledge graphs: entity-relationship reasoning

Production Challenges

Q: How do you evaluate RAG quality?

Metrics to discuss:

• **Retrieval**: Precision@k, Recall@k, MRR, NDCG
• **Generation**: Faithfulness, relevance, answer correctness
• **End-to-end**: User satisfaction, task completion
• **Tools**: RAGAS, TruLens, custom evaluation pipelines

Q: How do you handle documents that change frequently?

Strong Answer: "For frequently changing documents, I implement incremental indexing with change detection. Track document hashes or modification times. When changes occur, re-chunk and re-embed only affected documents. Use vector database features for upsert operations. For real-time requirements, consider streaming architectures. Also implement cache invalidation for any cached responses based on affected documents."

Q: What are common failure modes in RAG systems?

Critical failures to know:

• **Retrieval miss**: Relevant content exists but isn't retrieved
• **Wrong chunk**: Retrieved content is superficially similar but wrong
• **Context overflow**: Too much context degrades generation
• **Hallucination despite context**: Model ignores retrieved content
• **Stale information**: Retrieved content is outdated

System Design Question

"Design a RAG system for a company's internal knowledge base with 10,000 documents."

Key points to cover:

1. Document processing pipeline (formats, parsing, chunking)
2. Embedding strategy (model choice, batch processing)
3. Vector store selection (scale, features, cost)
4. Retrieval optimization (hybrid search, reranking)
5. Context management (length limits, selection)
6. Access control (who can see what documents)
7. Freshness handling (updates, deletions)
8. Monitoring and evaluation
9. Cost optimization

Practical Experience

Be ready to discuss:

• Chunking strategies you've tried and results
• How you debugged poor retrieval quality
• Trade-offs you made for latency vs quality
• How you handled edge cases (tables, images, code)

RAG seems simple but production RAG is full of nuances. Demonstrating hands-on experience with these challenges sets you apart.