Tools / AI RAG Interview questions
Beginner RAG Interview Questions
Q1: What is RAG?Retrieval-Augmented Generation is a technique that combines a retrieval system (to fetch relevant information from a knowledge base) with a generative model (like an LLM) to provide more accurate, context-aware answers.
Q2: Why is RAG preferred over fine-tuning for knowledge updates?RAG allows for real-time knowledge updates without re-training the model. Fine-tuning is computationally expensive and "bakes" information in, while RAG simply retrieves it as needed.
Q3: What are the three main components of a basic RAG pipeline?The document/knowledge source, the retrieval mechanism (search), and the generative model (LLM).
Q4: What is an embedding?An embedding is a numerical representation (vector) of text that captures semantic meaning, allowing computers to measure similarity between different pieces of data.
Q5: What is a vector database?A database designed to store, index, and query high-dimensional vectors, optimized for fast similarity searches.
Q6: What is a "context window" in an LLM?It is the limit of the amount of text (tokens) the model can process at once. RAG helps by providing only the most relevant snippets to fit within this window.
Q7: What is "chunking" in RAG?The process of breaking large documents into smaller, manageable pieces (chunks) so that the retriever can find specific information effectively.
Q8: What does "grounding" mean in RAG?It refers to using retrieved, factual documents to constrain an LLM's response, significantly reducing the chances of the model hallucinating.
Q9: How do you measure the success of a RAG system?Using metrics like retrieval precision (did we get the right document?) and generation faithfulness (did the LLM accurately use the provided document?).
Q10: Can RAG be used with any LLM?Yes, RAG is model-agnostic; it can be implemented with any LLM capable of accepting context as input.
Intermediate RAG Interview Questions
Q1: What is the difference between dense and sparse retrieval?Sparse retrieval (e.g., BM25) uses keyword matching, while dense retrieval (e.g., Vector search) uses semantic embeddings.
Q2: What is a hybrid search?A search strategy that combines keyword-based (sparse) and semantic (dense) search to leverage the strengths of both, often using Reciprocal Rank Fusion (RRF).
Q3: What are the challenges of fixed-size chunking?It may cut off sentences or split context mid-thought, leading to poor semantic representation.
Q4: How can metadata filtering improve RAG?By filtering the search space based on attributes like date, author, or category, you reduce noise and improve the accuracy of the retrieved context.
Q5: What is Re-ranking?A post-retrieval step where a more precise, but slower, model scores the top-N retrieved results to ensure the most relevant ones are passed to the LLM.
Q6: What is "context stuffing"?Adding too much irrelevant or redundant information into the prompt, which can confuse the LLM and degrade response quality.
Q7: Describe the Parent Document Retrieval pattern.Storing small chunks for retrieval but passing larger "parent" documents (or summaries) to the LLM to provide more context.
Q8: How do you handle multi-modal RAG?By creating embeddings for different data types (images, tables, text) within the same vector space, allowing the retriever to fetch non-textual data.
Q9: What is Query Expansion?A technique where the system rewrites or expands the user's initial query into multiple variations to improve retrieval coverage.
Q10: What is the role of an orchestrator framework like LangChain or LlamaIndex?They provide the abstractions and glue code necessary to chain together document loading, splitting, retrieval, and generation.
Advanced RAG Interview Questions
Q1: Explain Self-RAG.A framework where the model learns to evaluate its own retrieval and generation processes, deciding when to retrieve and critiquing its own output for quality.
Q2: How do you mitigate the "lost-in-the-middle" phenomenon?This occurs when LLMs ignore information in the middle of long prompts. Techniques include optimizing document ordering or using compression/summarization.
Q3: What is "Agentic RAG"?Using an AI agent that can plan, reason, and perform iterative retrieval steps (loops) rather than just a single retrieval call.
Q4: Discuss the trade-offs of using LLM-generated summaries vs. raw chunks.Summaries provide global context but may lose crucial details; raw chunks provide precision but may lack the "big picture" context.
Q5: How does recursive retrieval differ from standard retrieval?Recursive retrieval involves traversing a graph of documents (e.g., summaries linked to sub-documents) to find the most granular information.
Q6: What is graph-based RAG (GraphRAG)?Integrating Knowledge Graphs with vector search to capture explicit relationships between entities, helping with multi-hop reasoning questions.
Q7: How do you perform "RAG evaluation" without ground truth?By using frameworks like RAGAS or TruLens, which use an LLM-as-a-judge to measure faithfulness, answer relevance, and context precision.
Q8: Explain the concept of "ColBERT" and late interaction.ColBERT uses a late-interaction mechanism to compare token-level embeddings, offering higher precision than standard single-vector embeddings.
Q9: How do you handle retrieval in a highly dynamic, streaming data environment?Requires low-latency ingestion, CDC (Change Data Capture) into the vector store, and potential use of in-memory vector indexes.
Q10: What are the security risks associated with RAG?Prompt injection (manipulating the retrieved context), data leakage (retrieving information the user shouldn't see), and indirect prompt injection through poisoned documents.
