LLM & GenAI Engineer Interview Questions

The definitive guide to LLM architecture, Prompt Engineering, RAG, Fine-tuning, Agents, and Production MLOps. Essential for acing GenAI roles in 2026.

Total Questions:260
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1.Explain how GPT (Generative Pre-trained Transformer) works to a non-technical person.

2.What's the difference between GPT-3.5, GPT-4, and GPT-4 Turbo?

3.How does the Transformer architecture work? Explain self-attention.

4.What's the difference between encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5) models?

5.What's tokenization and why does it matter in LLMs?

6.Explain byte-pair encoding (BPE) vs WordPiece vs SentencePiece.

7.What's the context window and why is it important?

8.How do you handle inputs longer than the context window?

9.What's positional encoding in Transformers?

10.Explain the difference between absolute and relative positional encoding.

11.What's the role of the feedforward layer in Transformers?

12.How does multi-head attention work?

13.What's the difference between masked and causal attention?

14.Why do we use layer normalization in Transformers?

15.What's the purpose of the residual connections in Transformers?

16.What's prompt engineering and why is it important?

17.Explain zero-shot, one-shot, and few-shot prompting with examples.

18.What's chain-of-thought (CoT) prompting?

19.What's the difference between CoT and zero-shot CoT?

20.What's tree-of-thought prompting?

21.Explain ReAct (Reasoning and Acting) prompting.

22.What's the role of system prompts vs user prompts?

23.How do you prevent prompt injection attacks?

24.What's prompt leaking and how do you prevent it?

25.What are delimiters and why use them in prompts?

26.How do you structure prompts for better results?

27.What's role prompting (e.g., 'You are an expert...')?

28.How do you handle ambiguous user queries?

29.What's negative prompting?

30.How do you use examples effectively in prompts?

31.What's the difference between instructional and conversational prompts?

32.How do you optimize prompts for cost and quality?

33.What's prompt chaining?

34.Explain self-consistency in prompting.

35.What's constitutional AI prompting?

36.What's the difference between pre-training and fine-tuning?

37.When should you fine-tune vs use prompt engineering?

38.What's instruction tuning?

39.Explain RLHF (Reinforcement Learning from Human Feedback).

40.What's the difference between RLHF and supervised fine-tuning?

41.What's LoRA (Low-Rank Adaptation) and why use it?

42.How does QLoRA differ from LoRA?

43.What's PEFT (Parameter-Efficient Fine-Tuning)?

44.Explain adapter-based fine-tuning.

45.What's catastrophic forgetting in fine-tuning?

46.How do you prevent catastrophic forgetting?

47.What's the difference between full fine-tuning and PEFT?

48.What dataset size do you need for fine-tuning?

49.How do you prepare training data for fine-tuning?

50.What's data contamination and how do you avoid it?

51.What's the optimal learning rate for fine-tuning LLMs?

52.How many epochs should you fine-tune for?

53.What's warmup in training and why use it?

54.How do you evaluate a fine-tuned model?

55.What's alignment in LLMs?

36.What's the difference between pre-training and fine-tuning?

37.When should you fine-tune vs use prompt engineering?

38.What's instruction tuning?

39.Explain RLHF (Reinforcement Learning from Human Feedback).

40.What's the difference between RLHF and supervised fine-tuning?

41.What's LoRA (Low-Rank Adaptation) and why use it?

42.How does QLoRA differ from LoRA?

43.What's PEFT (Parameter-Efficient Fine-Tuning)?

44.Explain adapter-based fine-tuning.

45.What's catastrophic forgetting in fine-tuning?

46.How do you prevent catastrophic forgetting?

47.What's the difference between full fine-tuning and PEFT?

48.What dataset size do you need for fine-tuning?

49.How do you prepare training data for fine-tuning?

50.What's data contamination and how do you avoid it?

51.What's the optimal learning rate for fine-tuning LLMs?

52.How many epochs should you fine-tune for?

53.What's warmup in training and why use it?

54.How do you evaluate a fine-tuned model?

55.What's alignment in LLMs?

56.What's RAG and why is it important?

57.When should you use RAG vs fine-tuning?

58.Explain the RAG pipeline step-by-step.

59.What's chunking and why does chunk size matter?

60.What's the optimal chunk size for RAG?

61.What's chunk overlap and why use it?

62.How do you handle structured vs unstructured data in RAG?

63.What's embedding and how is it used in RAG?

64.What embedding models do you know?

65.What's the difference between dense and sparse embeddings?

66.What's cosine similarity and why use it for retrieval?

67.What's semantic search?

68.What's the difference between keyword search and semantic search?

69.What's a vector database?

70.Compare vector databases: Pinecone vs Weaviate vs Chroma vs Qdrant.

71.What's FAISS and when would you use it?

72.How do you choose k in top-k retrieval?

73.What's re-ranking and why is it important?

74.What's hybrid search (keyword + semantic)?

75.How do you handle multi-modal RAG (text + images)?

76.What's metadata filtering in vector search?

77.How do you update documents in a RAG system?

78.What's the cold start problem in RAG?

79.How do you evaluate RAG system performance?

80.What's retrieval precision vs recall in RAG?

81.How do you reduce hallucinations in RAG?

82.What's context stuffing in RAG?

83.How do you handle multi-document retrieval?

84.What's query transformation/rewriting?

85.What's HyDE (Hypothetical Document Embeddings)?

86.What's an LLM agent?

87.Explain the ReAct framework for agents.

88.What's function calling in LLMs?

89.How do you implement tool use with LLMs?

90.What's the difference between function calling and tool use?

91.How do you design tools for LLM agents?

92.What's LangChain and what problems does it solve?

93.What's LlamaIndex (formerly GPT Index)?

94.Compare LangChain vs LlamaIndex.

95.What's AutoGPT and how does it work?

96.What's the planning-execution loop in agents?

97.How do you handle agent errors and retries?

98.What's multi-agent systems?

99.How do you implement memory in LLM agents?

100.What's the difference between short-term and long-term memory?

101.What's conversation history management?

102.How do you implement agent observability?

103.What's agent evaluation metrics?

104.What's the difference between autonomous and semi-autonomous agents?

105.How do you prevent infinite loops in agents?

106.What's temperature in text generation?

107.How does temperature affect output randomness?

108.What's top-k sampling?

109.What's top-p (nucleus) sampling?

110.When would you use top-k vs top-p?

111.What's beam search?

112.What's the difference between greedy decoding and sampling?

113.What's repetition penalty?

114.What's frequency penalty vs presence penalty?

115.How do you control output length?

116.What's the role of max_tokens parameter?

117.What's early stopping in generation?

118.How do you ensure deterministic outputs?

119.What's logit bias and when would you use it?

120.What's constrained generation?

121.How do you deploy an LLM to production?

122.What's model serving for LLMs?

123.What's the difference between hosted APIs (OpenAI) vs. self-hosted models?

124.When should you use OpenAI API vs. open-source models?

125.How do you optimize LLM inference latency?

126.What's batching in LLM inference?

127.What's KV cache and how does it speed up inference?

128.What's quantization and how does it help?

129.Explain 8-bit vs. 4-bit quantization.

130.What's the trade-off between quantization and quality?

131.What's model distillation?

132.How do you reduce LLM API costs?

133.What's caching in LLM applications?

134.How do you implement semantic caching?

135.What's streaming responses and when to use it?

136.How do you handle rate limits with LLM APIs?

137.What's exponential backoff for API retries?

138.How do you implement fallback strategies for LLMs?

139.What's load balancing for LLM services?

140.How do you monitor LLM applications in production?

141.How do you evaluate LLM outputs?

142.What's BLEU score and is it good for LLMs?

143.What's ROUGE score?

144.What's perplexity?

145.How do you measure hallucination?

146.What's faithfulness in RAG evaluation?

147.What's answer relevancy?

148.What's context precision and recall in RAG?

149.How do you implement LLM-as-judge evaluation?

150.What's human evaluation vs automated evaluation?

151.What's A/B testing for LLM applications?

152.How do you track LLM performance over time?

153.What metrics do you use for chatbots?

154.How do you measure response quality?

155.What's the role of user feedback?

156.What's hallucination in LLMs and how do you reduce it?

157.What's prompt injection?

158.How do you prevent jailbreaking?

159.What's data privacy in LLM applications?

160.How do you handle PII (Personally Identifiable Information)?

161.What's model alignment?

162.What's red teaming for LLMs?

163.How do you implement content filtering?

164.What's toxicity detection?

165.How do you handle bias in LLMs?

166.What's constitutional AI?

167.How do you implement guardrails in LLM apps?

168.What's responsible AI deployment?

169.How do you handle controversial topics?

170.What's explainability in LLMs?

171.Write code to call OpenAI API.

172.Implement a basic chatbot with conversation history.

173.Code a RAG pipeline from scratch.

174.Write a function to chunk documents.

175.Implement semantic search with embeddings.

176.Code a simple LangChain agent.

177.Write prompt template with variables.

178.Implement streaming responses.

179.Code error handling for LLM API calls.

180.Write a retry mechanism with exponential backoff.

181.Implement token counting.

182.Code a prompt caching system.

183.Write a function to truncate text to token limit.

184.Implement conversation summarization.

185.Code a simple vector store.

186.Write evaluation metrics (cosine similarity).

187.Implement hallucination detection.

188.Code a multi-turn conversation handler.

189.Write unit tests for LLM functions.

190.Implement logging for LLM calls.

191.Design a customer support chatbot system.

192.Design a document Q&A system (like ChatPDF).

193.Design a code generation assistant.

194.Design a content moderation system using LLMs.

195.Design a personalized email generation system.

196.Design a multi-language translation service.

197.Design a resume screening system.

198.Design a meeting summarization tool.

199.Design a SQL query generator from natural language.

200.Design a research assistant that searches academic papers.

201.How would you build a multi-tenant LLM application?

202.Design a system to handle millions of LLM requests.

203.How would you implement user-specific customization?

204.Design monitoring for LLM quality degradation.

205.How would you version control prompts in production?

206.What's the difference between GPT-4 and Claude?

207.What's Gemini (Google)?

208.What's LLaMA and LLaMA 2?

209.What's Mistral AI models?

210.What's Mixtral (Mixture of Experts)?

211.What's the difference between Llama-2-7B, 13B, and 70B?

212.What's Falcon LLM?

213.What's MPT (MosaicML)?

214.What's Vicuna?

215.What's Alpaca?

216.What's the difference between base models and instruct models?

217.What's chat-tuned vs base model?

218.What's Code Llama?

219.What's StarCoder?

220.What's Codex?

221.What's mixture of experts (MoE)?

222.How does sparse activation work in MoE?

223.What's retrieval-augmented pre-training?

224.What's continual learning for LLMs?

225.How do you adapt LLMs to new domains?

226.What's federated learning for LLMs?

227.What's on-device LLM inference?

228.What's speculative decoding?

229.What's flash attention?

230.What's grouped-query attention (GQA)?

231.What's sliding window attention?

232.What's RoPE (Rotary Position Embedding)?

233.What's ALiBi (Attention with Linear Biases)?

234.What's model merging?

235.What's GGUF format?

236.Your LLM is hallucinating 30% of the time. What do you do?

237.Your RAG system retrieves irrelevant documents. How do you fix it?

238.Your API costs are too high. How do you optimize?

239.Your users complain responses are too slow. What's your approach?

240.Your prompt works 80% of the time. How do you improve it?

241.You need to process 100k documents for RAG. What's your strategy?

242.Your model violates content policy. How do you handle it?

243.You need to support 50 languages. What's your approach?

244.Your embeddings don't capture domain-specific meaning. What do you do?

245.You need to explain LLM decisions to compliance team. How?

246.Your context window is full but conversation must continue. Solutions?

247.You detect prompt injection in production. Immediate steps?

248.Your fine-tuned model forgets general knowledge. What happened?

249.Users report inconsistent responses. How do you debug?

250.You need to migrate from GPT-3.5 to GPT-4. Migration strategy?

251.Walk me through an LLM project you built end-to-end.

252.What's the most challenging part of working with LLMs?

253.How do you stay updated with rapidly evolving LLM space?

254.Describe a time when your LLM application failed.

255.How do you explain LLM limitations to stakeholders?

256.What's your process for debugging LLM issues?

257.How do you balance innovation vs. production stability?

258.Describe your approach to prompt engineering.

259.How do you make build vs. buy decisions for LLM features?

260.What excites you most about LLMs?