RAG
28 bites tagged RAG: interview questions with model answers, and 60-second explainers.
Trade-offs between dense and sparse retrieval in RAG?
This question tests your grasp of information retrieval fundamentals and their practical trade-offs in a modern RAG system. A strong answer first defines dense (semantic) and sparse (keyword) retrieval, then contrasts their performance on different query types, and finally analyzes their operational costs (compute, storage, latency). A common red flag is declaring dense retrieval universally superior without acknowledging its weaknesses, particularly with keywords and identifiers.
RAG vs. Fine-Tuning: Key Differences
This tests your understanding of how LLMs incorporate knowledge, specifically the trade-offs between embedding it in model weights versus retrieving it at runtime. A great answer defines RAG as runtime retrieval from an external source and fine-tuning as baking knowledge into model parameters, then contrasts their approaches to knowledge updates, cost, and providing citations. A red flag is stating one is always better, or failing to explain that they solve different problems and can be used tog
RAG: Giving Language Models an Open-Book Exam
Retrieval-Augmented Generation (RAG) gives a language model an open-book exam instead of forcing it to memorize everything. It combines a model's reasoning ability with a searchable external knowledge base. This grounds LLM responses in specific, up-to-date information, like a support bot using a product manual. The footgun is forgetting that the quality of the retrieved information directly limits the quality of the final answer.
Vector Databases: Searching by Meaning, Not Matches
A vector database organizes data by meaning, not just exact values. Instead of finding a record by its ID, you find it by its similarity to a query. This powers AI features like Retrieval-Augmented Generation (RAG), where an LLM finds relevant documents, and recommendation engines. The main footgun is that it finds *approximate* matches, trading perfect accuracy for speed and the ability to search unstructured data.
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