Generative AI interview questions for experienced

1. 50 Generative AI Interview Questions & Answers 1. How do Transformers outperform RNNs/LSTMs in Generative AI? Transformers replace sequential processing with self-attention, allowing models to analyze all tokens simultaneously. This enables better long-range dependency capture, faster training, and scalability to billions of parameters. 2. Explain the Transformer architecture in detail. Transformers consist of encoder-decoder blocks with self-attention, feed-forward layers, residual connections, and layer normalization. Self-attention computes relationships between all tokens, while positional encoding preserves sequence order. 3. What is self-attention and why is it critical? Self-attention allows each token to weigh its importance relative to others in the sequence. This mechanism is crucial for understanding long sentences and complex reasoning. 4. What are attention heads and multi-head attention? Multi-head attention runs multiple attention mechanisms in parallel. This improves representation richness and helps capture syntactic and semantic relationships simultaneously 5. What limits the scaling of Large Language Models? Scaling is limited by compute cost, memory bandwidth, training data quality, energy consumption, and diminishing returns. Beyond a point, larger models yield marginal improvements unless paired with better data and architectures.

2. 6. What is Mixture of Experts (MoE) architecture? MoE models consist of multiple expert networks where only a subset is activated per input. This increases model capacity without proportional compute cost. 7. What is RLHF and how does it work? Reinforcement Learning from Human Feedback aligns model behavior with human preferences. Human evaluators rank outputs, a reward model is trained, and reinforcement learning optimizes the LLM. 8. What is catastrophic forgetting? It occurs when fine-tuning on new data causes the model to lose previously learned knowledge. Techniques like regularization, replay buffers, and adapter-based tuning help mitigate this. 9. Explain LoRA and parameter-efficient fine-tuning. LoRA injects low-rank matrices into existing layers instead of updating full weights. This reduces memory usage, training cost, and deployment complexity while preserving performance. 10. What is gradient checkpointing? It reduces memory usage by storing fewer intermediate activations during forward pass and recomputing them during backpropagation. 11. What causes hallucinations in LLMs Hallucinations arise from probabilistic generation, lack of grounding, incomplete context, or overgeneralization. The model predicts plausible text rather than verifying facts.

3. 12. How do you systematically reduce hallucinations? Use RAG, constrained prompts, citations, structured outputs, verification layers, and human-in-the-loop review. Grounding responses in external data significantly improves factual accuracy. 13. What is Retrieval-Augmented Generation (RAG)? RAG combines LLMs with external knowledge retrieval systems. Relevant documents are fetched using embeddings and injected into prompts, enabling accurate and up-to-date responses without retraining. 14. What are common RAG failure modes? Poor chunking, low-quality embeddings, irrelevant retrievals, token overflow, and outdated indexes. These issues reduce answer quality despite correct generation. 15. How do you design an optimal chunking strategy? Chunks should follow semantic boundaries, balance size with overlap, and preserve context. Typical sizes range from 300–800 tokens with 10–20% overlap, tuned via retrieval evaluation. 16. Why are vector databases critical in RAG? They store embeddings and enable fast similarity search at scale. Vector DBs like Pinecone or FAISS make real-time retrieval feasible. 17. What is hybrid search? Hybrid search combines keyword-based (BM25) and vector-based semantic search. This improves recall and precision, especially for technical or domain-specific queries.

4. 18. What is agentic AI? Agentic AI systems can plan, reason, act, and adapt autonomously using tools, memory, and feedback loops. Unlike static prompts, agents execute multi-step workflows toward goals. 19. How does tool calling improve agent reliability? Tool calling delegates deterministic tasks (calculations, API calls, database queries) to external systems. This reduces hallucinations and improves accuracy and trustworthiness. 20. What are common agent failure modes? Infinite loops, tool misuse, hallucinated tools, poor planning, and missing stopping conditions. These are mitigated using constraints, retries, and monitoring. 21. How do you design guardrails for agentic systems? Restrict tool access, enforce policies, add timeouts, validate outputs, log actions, and include human approval for critical steps. 22. What is temperature and top-p sampling? Temperature controls randomness, while top-p limits token selection by cumulative probability. Lower values produce deterministic outputs; higher values increase creativity. 23. What is constrained decoding? constrained decoding enforces output formats using schemas, grammars, or rules. This is essential for JSON outputs, SQL generation, and enterprise workflows.

5. 24. How do you evaluate generative models? Use automated metrics (BLEU, ROUGE), factual accuracy checks, human evaluation, task success rates, and business KPIs. 25. What is chain-of-thought prompting? chain-of-thought prompting encourages step-by-step reasoning. While it improves accuracy, exposing reasoning can increase security and prompt injection risks. 26. How do you optimize LLM inference latency? Apply batching, caching, quantization, distillation, and speculative decoding. Infrastructure tuning also plays a key role. 27. What is quantization? Quantization reduces numerical precision (FP32 → INT8/INT4), lowering memory and speeding inference with minimal accuracy loss. 28. What is model distillation? A smaller model learns to mimic a larger one, preserving performance while reducing cost and latency. 29. What is speculative decoding? A smaller model predicts tokens ahead of a larger model, which then verifies them. This accelerates generation significantly. 30. What challenges exist in multi-tenant LLM systems? Resource isolation, data leakage, cost attribution, fairness, and latency management across users.

6. 31. How do you secure LLM applications? Use access controls, encryption, prompt validation, tool restrictions, logging, and continuous monitoring. 32. What is prompt injection and how do you prevent it? Prompt injection manipulates instructions via user input. Prevent it using role separation, input sanitization, and output validation. 33. What is differential privacy in GenAI? Differential privacy adds controlled noise to data or outputs to protect individual privacy while retaining statistical usefulness. 34. What is AI red-teaming? AI red-teaming involves adversarial testing of AI systems to identify safety, bias, and misuse vulnerabilities before deployment. 35. What is AI observability? Monitoring prompts, outputs, latency, errors, and drift in real time to maintain system reliability. 36. How do you detect model drift? Track changes in output quality, embeddings distribution, user feedback, and retrieval relevance over time. 37. What is feedback-loop learning? Feedback-loop learning uses user feedback to improve prompts, retrieval strategies, or fine-tuning pipelines continuously.

7. 38. Why is human-in-the-loop critical? Humans validate high-risk outputs, ensure ethical compliance, and maintain trust in AI-driven decisions. 39. What is long-context reasoning? Handling very large input contexts efficiently while preserving relevance and coherence—a major challenge in LLM design. 40. How will multimodal models impact GenAI? They enable unified reasoning across text, image, audio, and video, unlocking advanced applications like autonomous agents and copilots. 41. What is constitutional AI? Aligning models using predefined ethical rules instead of direct human feedback, improving scalability and consistency. 42. What is self-reflection in LLMs? Models analyze and refine their own outputs, improving accuracy and reasoning quality. 43. How do you prevent data leakage during fine-tuning? Apply anonymization, data governance policies, validation checks, and restricted access controls. 44. What is autonomous error recovery in agents? Agents detect failures, re-plan steps, and retry actions without human intervention.

8. 45. What skills define senior GenAI engineers? System design, optimization, safety, evaluation, cloud infrastructure, and governance expertise. 46. What is enterprise GenAI governance? Policies covering data usage, compliance, ethics, monitoring, and accountability. 47. What is the cost optimization strategy for GenAI? Reduce token usage, cache results, use smaller models, and optimize inference pipelines. 48. How do you choose open-source vs closed models? Evaluate cost, performance, compliance, customization, and data control requirements. 49. What industries lead GenAI adoption? ● IT ● finance, ● healthcare, ● marketing, ● customer support, ● Analytics. 50. What is the biggest challenge in enterprise GenAI adoption? Balancing rapid innovation with safety, cost control, governance, and regulatory compliance.