Table of Contents
Most LLM applications in production are RAG systems. Yet many candidates claim RAG experience but can't answer basic questions about chunking strategies or retrieval evaluation. Here's how to separate real RAG expertise from resume padding.
Chunking Strategy
Look for: context window of LLM, nature of documents (paragraphs vs tables vs code), semantic boundaries (sentences, paragraphs, sections). A/B test different sizes.
Embedding & Retrieval
Evaluate on your domain (MTEB benchmark), consider latency/cost trade-offs, multilingual needs, and vector dimension (affects storage and search speed).
Re-ranking & Hybrid Search
When keywords matter (product names, IDs, exact phrases) alongside semantic meaning. Combine BM25 (keyword) with vector similarity.
RAG Evaluation
Hit rate (relevant document in top-k), MRR (Mean Reciprocal Rank), NDCG. For end-to-end: answer correctness, faithfulness, answer relevancy.
Practical Assessment Exercise
- ✦ Provide a sample document corpus
- ✦ Ask candidate to design chunking strategy
- ✦ Require retrieval architecture explanation
- ✦ Ask how they would measure retrieval quality
- ✦ Discuss hallucination mitigation techniques
RAG Red Flags
- ✦ Cannot explain chunk overlap decisions
- ✦ No understanding of embedding evaluation
- ✦ Only uses vector search without justification
- ✦ No familiarity with re-ranking
- ✦ Never measured retrieval performance
Find RAG Experts
RAG is subtle. Small changes in chunking or retrieval dramatically impact output quality. Hire engineers who understand these trade-offs. Offline Pixel pre-vets RAG expertise before you interview.
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