Top Generative AI Interview Questions and Answers
Top Generative AI Interview Guide
From Transformers to Agentic Workflows. Master the concepts defining the next era of intelligence.
01 Architecture & Core Concepts
1. Explain the "Self-Attention" mechanism in Transformers.
Self-attention allows a model to weigh the importance of different words in a sequence relative to a specific word. It uses Query (Q), Key (K), and Value (V) vectors to calculate attention scores: $$ \text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V $$
2. What is the difference between Discriminative and Generative AI?
Discriminative models learn the boundary between classes ($P(y|x)$) to classify data. Generative models learn the distribution of the data ($P(x)$ or $P(x|y)$) to create new, similar instances.
02 RAG, Fine-Tuning & Optimization
3. RAG vs. Fine-Tuning
RAG (Retrieval-Augmented Generation): Connects the LLM to external, real-time data sources. Best for factual accuracy.
Fine-Tuning: Adjusts model weights on a specific dataset. Best for changing the model's style or behavior.
4. What is LoRA?
Low-Rank Adaptation (LoRA) is a PEFT (Parameter-Efficient Fine-Tuning) technique. Instead of updating all billions of parameters, it adds small, trainable rank-decomposition matrices to existing layers, drastically reducing VRAM requirements.
High-Frequency Interview Topics
| Concept | Key Focus Area |
|---|---|
| Hallucinations | How to mitigate using Temperature settings and Grounding. |
| Tokenization | Byte-Pair Encoding (BPE) vs WordPiece. |
| RLHF | Reinforcement Learning from Human Feedback and Reward Models. |
| Chain of Thought | Prompt engineering techniques to improve reasoning. |
| Vector DBs | How Pinecone/Milvus facilitate semantic search in RAG. |
| Diffusion Models | Forward noise process vs Reverse denoising process. |
| Agentic AI | Tool-use, planning, and recursive self-correction. |
The "Senior" Differentiator
Modern interviewers look for AI Safety and Deployment knowledge:
- ✔ Prompt Injection: Methods to defend against malicious input.
- ✔ Quantization: Reducing FP32 to INT8 for edge deployment.
- ✔ Context Windows: Challenges with "Lost in the Middle" phenomenon.
- ✔ Evaluation: Using LLM-as-a-judge (e.g., G-Eval) vs BLEU/ROUGE.
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