RAG System Design Interview:
What You Need to Know

Common questions: Design a customer support RAG system, How would you handle multi-document queries, Design RAG for a legal document search, How do you evaluate RAG quality, Design RAG with access control, How would you reduce hallucinations in RAG. Interviewers test your ability to make trade-offs, not memorize one architecture.

Cover multiple approaches: fixed-size chunks (simple but breaks context), semantic chunking (better coherence but slower), recursive chunking (hierarchical but complex). Discuss trade-offs: smaller chunks = more precise retrieval but less context, larger chunks = more context but noisier results. Mention overlap strategies to maintain continuity. Show you understand there's no universal best approach.

Recommend hybrid search when: documents contain technical terms or proper nouns (keyword search helps), users ask questions with exact phrases, you need to handle both semantic and keyword queries. Explain the architecture: combine BM25/keyword scores with vector similarity scores. Mention that hybrid often outperforms pure vector search in production.

Use reranking when: initial retrieval returns many borderline-relevant results, you need high precision over recall, latency budget allows for extra processing step. Explain the two-stage approach: fast bi-encoder retrieval gets top 50-100 candidates, slower cross-encoder reranking selects final top-k. Trade-off: better relevance vs. added latency (100-300ms typically).

Discuss: (1) Caching—cache embeddings, cache frequent queries, cache LLM responses, (2) Async processing—queue ingestion, batch embeddings, (3) Vector DB scaling—sharding strategies, approximate nearest neighbor trade-offs, (4) LLM costs—when to use smaller models, prompt caching. Show you've thought about these even if you haven't implemented at scale.

With general AI knowledge: 1-2 weeks focused on RAG. Spend time: (1) Building a simple RAG system end-to-end, (2) Reading engineering blogs about production RAG, (3) Understanding each component's trade-offs, (4) Practicing explaining architecture decisions out loud. Hands-on experience, even small-scale, dramatically improves interview performance.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

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Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.