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.

It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.

Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.

You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.

It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.

Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.