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Interview-ready list of Generative AI & RAG (Retrieval-Augmented Generation) Questions

 

🧠 Generative AI – Core Interview Questions

Fundamentals

  1. What is Generative AI, and how is it different from traditional AI?

  2. How do generative models differ from discriminative models?

  3. What are common types of generative models?

  4. Explain the concept of probability distribution learning in GenAI.

  5. What is autoregressive generation?

  6. What is temperature in text generation?

  7. What is top-k and top-p (nucleus) sampling?

  8. How does beam search work, and when is it useful?

  9. What causes hallucinations in generative models?

  10. How do you evaluate generative AI outputs?

LLM Architecture & Training

  1. Explain the Transformer architecture.

  2. Why is self-attention better than RNNs for language tasks?

  3. What is positional encoding?

  4. Difference between encoder-only, decoder-only, and encoder-decoder models.

  5. Why are decoder-only models used for LLMs?

  6. What is pre-training vs fine-tuning?

  7. What is instruction tuning?

  8. What is RLHF (Reinforcement Learning from Human Feedback)?

  9. What is catastrophic forgetting?

  10. How do scaling laws apply to LLMs?

Prompt Engineering

  1. What is prompt engineering?

  2. Zero-shot vs one-shot vs few-shot prompting.

  3. What is chain-of-thought prompting?

  4. What is self-consistency prompting?

  5. How do system prompts differ from user prompts?

  6. How do you reduce hallucinations using prompts?

  7. What are prompt injection attacks?

  8. How do you protect LLMs from prompt leakage?

📚 RAG (Retrieval-Augmented Generation) – Core Questions

RAG Fundamentals

  1. What is Retrieval-Augmented Generation (RAG)?

  2. Why is RAG needed if LLMs are powerful?

  3. Explain the RAG pipeline step by step.

  4. How does RAG reduce hallucinations?

  5. Difference between fine-tuning and RAG.

  6. When should you prefer RAG over fine-tuning?

  7. What types of data are best suited for RAG?

  8. What is grounding in RAG systems?

Embeddings & Vector Search

  1. What are embeddings?

  2. How are text embeddings generated?

  3. What is cosine similarity?

  4. Difference between cosine similarity and dot product.

  5. What is semantic search?

  6. What are vector databases?

  7. Popular vector databases used in RAG.

  8. How does ANN (Approximate Nearest Neighbor) search work?

  9. What is HNSW indexing?

  10. How do you choose embedding dimensions?

Chunking & Indexing

  1. What is document chunking in RAG?

  2. Why is chunk size important?

  3. Fixed vs semantic chunking.

  4. What is overlap in chunking?

  5. How do you handle tables and PDFs in RAG?

  6. How do you index multi-modal data?

  7. How do you handle document updates in RAG?

⚙️ Advanced RAG Questions

  1. What is hybrid search?

  2. How does keyword + vector search improve retrieval?

  3. What is reranking in RAG?

  4. What are cross-encoders vs bi-encoders?

  5. How do you improve retrieval precision?

  6. What is query expansion?

  7. What is multi-hop RAG?

  8. How does agent-based RAG work?

  9. What is self-RAG?

  10. What is recursive retrieval?

  11. How do you handle long-context limitations?

  12. How do you prevent outdated knowledge in RAG?

  13. How do you evaluate RAG systems?

  14. What metrics are used for RAG evaluation?

🏗️ System Design & Real-World Questions

  1. Design a RAG system for an internal company knowledge base.

  2. How would you build a RAG chatbot for legal/medical data?

  3. How do you ensure data privacy in RAG?

  4. How do you handle access control in RAG?

  5. How do you reduce latency in a RAG pipeline?

  6. How do you scale RAG for millions of documents?

  7. How do you cache RAG responses?

  8. How do you handle multilingual RAG?

  9. How do you detect hallucinations at runtime?

  10. How would you deploy a RAG system in production?

  11. What are common failure modes of RAG systems?

🧪 Fine-Tuning vs RAG vs Tools

  1. Fine-tuning vs RAG vs function calling – compare.

  2. When does fine-tuning outperform RAG?

  3. What is LoRA and PEFT?

  4. Can RAG and fine-tuning be combined?

  5. What is tool-augmented generation?

  6. Difference between RAG and search-based QA systems.

  7. How does RAG differ from traditional IR systems?

🔐 Security, Ethics & Governance

  1. What are the security risks in GenAI systems?

  2. How do you prevent data leakage in RAG?

  3. What is model inversion?

  4. What is training data poisoning?

  5. How do you ensure explainability in RAG?

  6. How do you log and audit LLM outputs?

  7. Ethical risks of generative AI in enterprise use.

🚀 Practical / Coding / Debugging

  1. How would you debug poor RAG answers?

  2. What causes irrelevant context retrieval?

  3. How do you improve answer faithfulness?

  4. How do you handle noisy documents?

  5. What happens if embeddings are poor quality?

  6. How do you monitor RAG performance in production?

  7. What tools/frameworks have you used for RAG?

  8. Explain a GenAI or RAG project you’ve built end-to-end.


🧠 Advanced Generative AI (Deep Dive)

Model Behavior & Internals

  1. Why do LLMs hallucinate even with correct context?

  2. What is exposure bias in language models?

  3. Explain tokenization and its impact on model performance.

  4. BPE vs WordPiece vs SentencePiece.

  5. How does context window size affect reasoning?

  6. Why do longer prompts sometimes degrade output quality?

  7. What is attention collapse?

  8. How does KV-cache improve inference speed?

  9. What is speculative decoding?

  10. What is logit biasing?

Training & Optimization

  1. What is gradient checkpointing?

  2. Why mixed-precision training (FP16/BF16) is used?

  3. What is instruction overfitting?

  4. What is dataset contamination?

  5. What is alignment tax?

  6. What is model distillation?

  7. What is continual learning in LLMs?

  8. What are synthetic datasets in GenAI?

  9. How do you detect memorization in LLMs?

  10. What is data deduplication and why is it critical?

📚 RAG – Expert Level Questions

Retrieval Quality

  1. What causes retrieval drift?

  2. What is embedding space collapse?

  3. How do domain-specific embeddings outperform general ones?

  4. What is dense vs sparse retrieval?

  5. What is BM25 and why still relevant?

  6. What is score normalization in hybrid search?

  7. How do you handle contradictory retrieved documents?

  8. What is passage-level vs document-level retrieval?

  9. How do you rank retrieved chunks for reasoning?

  10. How do you handle irrelevant but high-similarity chunks?

Chunking & Knowledge Engineering

  1. How do you chunk code repositories?

  2. How do you chunk legal contracts?

  3. Sentence-based vs paragraph-based chunking.

  4. What is adaptive chunking?

  5. How do you handle metadata-aware retrieval?

  6. How do you store citations in RAG?

  7. What is context window budgeting?

  8. What is dynamic context injection?

  9. How do you merge overlapping chunks?

  10. What is hierarchical RAG?

🤖 Agentic RAG & Tooling

  1. What is agentic RAG?

  2. Planner–Executor architecture in GenAI.

  3. How do agents decide when to retrieve?

  4. What is tool hallucination?

  5. How do you constrain agent actions?

  6. What is ReAct prompting?

  7. What is memory in LLM agents?

  8. Short-term vs long-term memory in agents.

  9. How do agents update knowledge stores?

  10. Failure modes of autonomous agents.

🧪 Evaluation & Observability

  1. How do you evaluate factual consistency?

  2. What is answer faithfulness vs relevance?

  3. What is context precision and recall?

  4. What is RAGAS?

  5. Offline vs online evaluation of RAG.

  6. How do you A/B test RAG pipelines?

  7. How do you detect silent failures?

  8. What is human-in-the-loop evaluation?

  9. How do you log embeddings safely?

  10. How do you monitor drift in production RAG?

⚙️ Performance, Scaling & Cost

  1. How do you reduce embedding computation cost?

  2. Cold start vs warm start in RAG.

  3. How do you shard vector databases?

  4. What is query fan-out?

  5. How do you reduce token usage?

  6. How do you compress retrieved context?

  7. What is late interaction retrieval?

  8. What is streaming generation?

  9. CPU vs GPU trade-offs in RAG.

  10. Cost optimization strategies for GenAI apps.

🔐 Security, Privacy & Compliance (Enterprise Focus)

  1. How do you prevent prompt injection via retrieved docs?

  2. What is retrieval poisoning?

  3. How do you sanitize documents before indexing?

  4. How do you enforce row-level security in RAG?

  5. How do you handle PII in embeddings?

  6. Can embeddings leak sensitive data?

  7. What is differential privacy in LLMs?

  8. What is red-teaming in GenAI?

  9. How do you implement audit trails?

  10. Compliance challenges (GDPR, HIPAA) in RAG.

🏗️ System Design – Hard Interview Questions

  1. Design a RAG system for 1B documents.

  2. Design a low-latency RAG chatbot (<300ms).

  3. Design a multi-tenant RAG SaaS platform.

  4. How would you design offline-first RAG?

  5. How would you version knowledge bases?

  6. How do you roll back faulty embeddings?

  7. How do you handle schema evolution?

  8. How do you test RAG pipelines automatically?

  9. How do you migrate vector DBs?

  10. How do you support real-time document ingestion?

🧠 Research-Oriented / Future GenAI

  1. What is long-context reasoning failure?

  2. Can RAG replace fine-tuning completely?

  3. What are memory-augmented transformers?

  4. What is retrieval-free reasoning?

  5. How do LLMs reason without retrieval?

  6. What is neuro-symbolic RAG?

  7. What is graph-based RAG?

  8. How can knowledge graphs enhance RAG?

  9. What are foundation model limitations?

  10. Where is GenAI heading post-2026?


🧠 Ultra-Advanced Generative AI

Reasoning & Cognition

  1. What is the difference between reasoning and pattern completion in LLMs?

  2. Why do LLMs fail at multi-step logical consistency?

  3. What is reasoning collapse?

  4. How does chain-of-thought differ from latent reasoning?

  5. What is tree-of-thought prompting?

  6. What is graph-of-thought reasoning?

  7. How do LLMs approximate symbolic reasoning?

  8. What are the limits of in-context learning?

  9. Why does reasoning degrade with longer contexts?

  10. Can LLMs truly generalize beyond training data?

Memory & Context

  1. What is external memory in GenAI?

  2. Short-term vs persistent memory in LLM systems.

  3. How do memory retrieval strategies differ from RAG?

  4. What is episodic memory in AI agents?

  5. How do you prevent memory poisoning?

  6. How do you age or forget memory safely?

  7. What is memory compression?

  8. How do you summarize without information loss?

  9. What is selective recall?

  10. Memory vs fine-tuning trade-offs.

📚 RAG – Cutting-Edge Architectures

Advanced Retrieval

  1. What is late fusion vs early fusion retrieval?

  2. What is ColBERT and late interaction?

  3. How does cross-attention reranking improve relevance?

  4. What is retrieval-time reasoning?

  5. What is query decomposition?

  6. What is sub-question retrieval?

  7. How do you support reasoning across documents?

  8. What is evidence aggregation?

  9. How do you detect missing evidence?

  10. What is retrieval abstention?

Knowledge Representation

  1. Unstructured RAG vs structured RAG.

  2. How do knowledge graphs integrate with RAG?

  3. What is schema-aware retrieval?

  4. What is entity-centric chunking?

  5. How do you resolve entity ambiguity?

  6. How do you handle temporal knowledge?

  7. How do you manage conflicting facts over time?

  8. What is provenance tracking?

  9. How do you ensure citation faithfulness?

  10. What is trust-aware RAG?

🤖 Agentic Systems – Hard Questions

  1. What is multi-agent collaboration?

  2. When should you use agents vs pipelines?

  3. How do agents negotiate task ownership?

  4. What is agent orchestration?

  5. How do agents share memory?

  6. What is tool planning vs tool execution?

  7. How do agents recover from tool failure?

  8. What is reflection in agents?

  9. What is self-critique?

  10. How do you prevent infinite agent loops?

🧪 Evaluation – Research & Industry

  1. What is causal evaluation in GenAI?

  2. How do you measure reasoning quality?

  3. What is evidence sufficiency?

  4. How do you score partial correctness?

  5. What is contradiction detection?

  6. How do you evaluate uncertainty calibration?

  7. What is abstention-aware evaluation?

  8. How do you benchmark domain-specific RAG?

  9. Why automated evaluation often fails?

  10. What is adversarial evaluation?

⚙️ Systems, Performance & Infrastructure

  1. How do you design GenAI systems for low memory devices?

  2. What is edge-based RAG?

  3. How do you optimize KV-cache memory?

  4. What is flash attention?

  5. What is paged attention?

  6. How do you handle GPU memory fragmentation?

  7. What is inference batching?

  8. Throughput vs latency trade-offs.

  9. What is backpressure in GenAI systems?

  10. What are queue-based RAG architectures?

🔐 Security, Safety & Robustness

  1. What is model extraction risk?

  2. How do you defend against data exfiltration?

  3. What is indirect prompt injection?

  4. How do you sandbox tools safely?

  5. What is content provenance?

  6. How do you watermark LLM outputs?

  7. What is jailbreak detection?

  8. How do you enforce safe completions?

  9. What is policy-based generation?

  10. How do you handle malicious retrieval results?

🧠 Research Frontiers & 2026+ Topics

  1. What are retrieval-augmented transformers?

  2. What is end-to-end differentiable RAG?

  3. What is neural search + LLM convergence?

  4. Can RAG be trained jointly with LLMs?

  5. What is memory-augmented reasoning?

  6. What is world-model learning?

  7. Can LLMs develop internal knowledge graphs?

  8. What is self-supervised reasoning?

  9. What are the limits of scaling?

  10. Will GenAI replace symbolic AI?

🏗️ Brutal System Design (Principal-Level)

  1. Design a fault-tolerant GenAI platform.

  2. Design a RAG system with strict SLAs.

  3. Design a regulated GenAI system (banking/healthcare).

  4. How do you guarantee answer traceability?

  5. How do you handle legal liability of LLM outputs?

  6. How do you design GenAI rollback mechanisms?

  7. How do you run chaos testing on RAG?

  8. How do you handle partial system failures?

  9. How do you build explainable GenAI systems?

  10. What would you change in today’s RAG architectures?

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