RAG Questions Dominate LLM Interviews

Retrieval-Augmented Generation (RAG) was first formalized in a 2020 paper by Patrick Lewis and colleagues from Facebook AI Research. It enhances large language models by allowing them to pull in real-time information from external knowledge bases before generating a response, addressing issues like outdated knowledge and hallucination. The choice of a chunking strategy is a critical, non-trivial decision that directly impacts retrieval performance. Simply splitting documents into fixed-size pieces can sever important context, leading to fragmented and less meaningful retrievals. Advanced methods like hierarchical chunking, which respects the document's structure (sections, tables), are being explored to improve the relevance of retrieved passages. Hybrid search has emerged as a best practice because it combines the strengths of both keyword-based (lexical) and vector-based (semantic) search. While semantic search understands the meaning behind a query, it can sometimes miss specific keywords or entities; lexical search excels at these exact matches, and combining them provides more robust and accurate retrieval. Scaling a RAG system to handle millions of documents introduces significant architectural challenges beyond just storage. As the dataset grows, retrieval latency and declining query accuracy become major issues. Solutions involve distributed architectures, optimized indexing strategies like IVF_FLAT or HNSW, and careful data sharding to maintain performance. Evaluating a RAG system is a multi-faceted process that goes beyond simple accuracy. Key metrics assess the performance of both the retriever and the generator. For the retriever, metrics like context relevance and context recall are crucial, while the generator is evaluated on faithfulness (how well it sticks to the provided context) and answer relevance. The decision between using RAG and fine-tuning an LLM depends on the specific use case. RAG is generally preferred for applications requiring up-to-date information and where data changes frequently, as the knowledge base can be updated without the high cost of retraining the model. Fine-tuning, on the other hand, is better suited for teaching the model a new skill, style, or specific domain language where the underlying patterns are static.