Generative AI Interview Questions & Answers
Introduction to Generative Artificial Intelligence (GenAI) 2025
What is Generative AI?
- Generative Artificial Intelligence (GenAI) is a branch of AI that focuses on generating new and original content such as text, images, videos, audio, code, and 3D designs.
- It uses advanced machine learning models including transformers, diffusion models, GANs, and large language models (LLMs).
Why is Generative AI Important?
- GenAI is transforming industries—from healthcare and finance to entertainment, education, and software development.
- It powers widely used applications such as ChatGPT, DALL·E, Midjourney, GitHub Copilot, and OpenAI Sora.
- It automates creative and cognitive tasks like content generation, summarization, personalization, research assistance, and software development.
Who Needs to Learn Generative AI?
Generative AI is now an essential skill for professionals such as:
- AI Engineers
- Data Scientists
- Machine Learning Developers
- LLM Application Builders
- Prompt Engineers
- MLOps and LLMOps Specialists
- Product Managers and Tech Strategists working in AI
Why Interviewers Focus on Generative AI in 2025
- GenAI is at the center of modern AI products and innovations.
- Recruiters assess candidates on a range of GenAI topics, including:
- Prompt engineering techniques
- Fine-tuning and transfer learning
- Responsible AI and bias mitigation
- RAG (Retrieval-Augmented Generation)
- Evaluation metrics for generative models
- Transformer, diffusion, and agent-based architectures
- Prompt engineering techniques
What This Guide Offers
- A curated list of 150 interview questions and answers on Generative AI.
- Structured to reflect real-world interview patterns from beginner to expert levels.
- Useful for job seekers, internal team upskilling, academic preparation, and hands-on learning.
Who Should Use This Guide?
- Job seekers preparing for interviews in AI, machine learning, or data science roles.
- Engineering and product teams upskilling or evaluating candidates.
- Educators building AI and GenAI learning materials.
- Developers integrating GenAI models into their applications.
- Content creators and marketers looking to understand the mechanics behind AI tools.
Why Staying Ahead with GenAI Knowledge Matters
Mastery of Generative AI in 2025 will help you:
- Drive innovation in AI-based products and platforms.
- Customize and deploy LLMs responsibly and effectively.
- Remain competitive and future-ready in a rapidly evolving AI job market.
Generative AI Interview Questions
1. What is Generative AI?
Generative AI refers to models that learn from data and create new content like text, images, music, or code, mimicking real data distributions.
2. How is Generative AI different from traditional AI?
Traditional AI focuses on classification or prediction; generative AI creates original outputs by learning underlying data patterns.
3. What is the difference between generative and discriminative models?
Generative models learn P(X, Y) to generate data, while discriminative models learn P(Y|X) to classify or predict outcomes.
4. Name a few popular Generative AI tools.
GPT-4, DALL·E 3, Midjourney, Bard, Claude, ChatGPT, and Stable Diffusion.
5. What are the main types of Generative AI?
Text generation, image generation, audio generation, and code generation.
6. What is GPT?
GPT (Generative Pre-trained Transformer) is a large language model developed by OpenAI that generates human-like text.
7. How does Generative AI learn?
It learns by training on massive datasets using unsupervised or self-supervised methods to recognize and recreate data patterns.
8. What is the role of unsupervised learning in Generative AI?
It enables the model to learn data distribution and generate outputs without needing labeled datasets.
9. What are some common applications of Generative AI?
Content writing, chatbots, virtual assistants, AI art, music composition, and drug discovery.
10. What’s the difference between GPT-3.5 and GPT-4?
GPT-4 is larger, more accurate, and better at handling complex reasoning and multilingual tasks compared to GPT-3.5.
11. Is Generative AI only about text generation?
No, it spans across images, video, audio, and even 3D model generation.
12. How do Large Language Models (LLMs) fit into Generative AI?
LLMs are a subset of Generative AI models trained on massive text corpora to generate human-like language.
13. What is zero-shot generation in AI?
It’s when a model performs a task it hasn’t been explicitly trained on by generalizing from its pretraining data.
14. What is fine-tuning in the context of Generative AI?
It’s the process of adapting a pre-trained model to a specific task or domain using a smaller dataset.
15. Why is Generative AI considered transformative in 2025?
It automates creativity, improves efficiency, and is powering everything from virtual assistants to AI copilots in enterprise software.
16. What are hallucinations in Generative AI?
Incorrect or fabricated outputs generated by models, often sounding plausible but factually wrong.
17. Can Generative AI understand context?
Yes, especially with transformer-based models which use attention mechanisms to retain context across long inputs.
18. What is the Turing Test and how does Generative AI relate?
It’s a measure of machine intelligence — if a human can’t distinguish between AI and a human, the AI passes. LLMs now approach this benchmark.
19. Is Generative AI dangerous?
It has risks — misinformation, deepfakes, copyright issues — but with guardrails, it can be safely deployed.
20. What are embeddings in Generative AI?
Vector representations of text or images that encode semantic meaning and are crucial for model understanding.
Transformers & Large Language Models
21. What is a Transformer model?
A Transformer is a deep learning model architecture based on self-attention, enabling parallel processing of sequences and long-range dependencies.
22. What is self-attention in Transformers?
It’s a mechanism that allows the model to weigh the relevance of different words in a sentence when encoding a given word.
23. What is the difference between BERT and GPT?
BERT is a bidirectional encoder for understanding text (used for classification), while GPT is an autoregressive decoder designed to generate text.
24. What is positional encoding in Transformers?
It adds information about the position of tokens in the sequence to allow the model to understand order, since Transformers lack recurrence.
25. What is the architecture of GPT models?
GPT uses a decoder-only Transformer stack with masked self-attention to enable text generation in a left-to-right manner.
26. How are tokens generated in GPT?
Tokens are generated one at a time using the model’s probability distribution, conditioned on previously generated tokens.
27. What is masked language modeling (MLM)?
A training technique (used in BERT) where some words are masked in the input and the model learns to predict them.
28. What is causal language modeling (CLM)?
A training technique (used in GPT) where each word is predicted based on the preceding context only.
29. What is tokenization in LLMs?
The process of converting input text into smaller units (tokens), like words or subwords, before feeding them into the model.
30. What is the vocabulary size of a typical LLM?
It ranges from 30,000 to 100,000 tokens, depending on the tokenizer and language model used.
31. What is temperature in language generation?
A parameter controlling randomness — lower values make output deterministic; higher values make it more creative.
32. What are Top-k and Top-p sampling?
Top-k samples from the top k likely tokens; Top-p (nucleus sampling) samples from the smallest set of tokens whose cumulative probability exceeds p.
33. What is beam search in language generation?
A decoding strategy that explores multiple sequences simultaneously and selects the best based on overall probability.
34. What is attention masking in GPT models?
It prevents the model from seeing future tokens during training to ensure it learns sequential dependencies correctly.
35. What are key, query, and value in attention mechanisms?
These are vectors derived from inputs; attention scores are calculated using queries and keys, and applied to values.
36. What are the limitations of Transformers?
They can be compute-intensive, struggle with long context lengths, and require large training datasets.
37. What is a multi-head attention mechanism?
It allows the model to learn information from different representation subspaces and focus on various parts of the sequence simultaneously.
38. What is cross-attention in multimodal Transformers?
A mechanism where input from one modality (like text) attends to another modality (like image) for joint understanding.
39. What is the context window in GPT models?
The maximum number of tokens a model can process at once; e.g., GPT-4 Turbo supports up to 128k tokens.
40. What is the difference between GPT-3.5 and GPT-4?
GPT-4 has better reasoning, supports more modalities, and is more aligned with human intent compared to GPT-3.5.
41. What is fine-tuning in Transformers?
Adapting a pretrained model to a specific task or domain using additional training data.
42. What are adapter layers in LLM fine-tuning?
Lightweight layers added to frozen Transformers to enable efficient training for downstream tasks.
43. What is the role of layer normalization in Transformers?
It stabilizes and speeds up training by normalizing the inputs across each layer.
44. How does the transformer decoder differ from encoder?
The decoder uses masked self-attention to allow autoregressive generation; encoder uses full self-attention for bidirectional context.
45. What are position-wise feedforward networks?
Fully connected layers applied to each position in the sequence independently to increase model capacity.
46. What is pretraining in LLMs?
The initial phase where the model learns language representations from massive unlabeled data before fine-tuning.
47. What is instruction tuning?
A method of fine-tuning LLMs to follow natural language instructions using curated prompt-response data.
48. What is multi-modal generative AI?
Models that understand and generate across multiple modalities, such as combining text, images, and audio.
49. What is gradient checkpointing in large models?
A memory-saving technique that recomputes intermediate results during backpropagation instead of storing all activations.
50. What are the ethical risks of LLMs?
Bias, misinformation, toxic content, hallucinations, and over-reliance on synthetic data.
Prompt Engineering Interview Questions
51. What is prompt engineering?
Prompt engineering is the practice of designing and optimizing input prompts to guide large language models (LLMs) toward desired outputs.
52. What is zero-shot prompting?
Zero-shot prompting is when you give the model a task without any examples and expect it to complete it based only on instructions.
53. What is few-shot prompting?
Few-shot prompting includes a few examples along with the prompt to help the model understand the pattern of the desired response.
54. What is chain-of-thought prompting?
A prompting technique where the model is guided to break down reasoning steps logically, improving performance on complex tasks.
55. What is retrieval-augmented generation (RAG)?
RAG is a method that combines retrieval systems (e.g., vector databases) with LLMs to provide factual context in real-time responses.
56. What is a prompt template?
A reusable prompt format that includes placeholders (e.g., for questions, contexts) to consistently generate outputs across examples.
57. What is prompt injection?
A security vulnerability where malicious users inject instructions into prompts to manipulate model behavior or leak information.
58. How can prompt injection be mitigated?
Using input sanitization, restricting model capabilities, separating user instructions from system prompts, and auditing responses.
59. What is a system prompt?
A hidden instruction sent to the model to control its behavior, tone, or role before processing user prompts.
60. How do you evaluate prompt quality?
By measuring output relevance, consistency, correctness, diversity, and user satisfaction, sometimes using A/B testing.
61. What is prompt tuning?
A technique where learnable prompt embeddings are optimized (instead of raw text) to guide model behavior during training.
62. What is the difference between soft and hard prompts?
Hard prompts are written in natural language; soft prompts are learned vectors optimized via training.
63. Can prompts be fine-tuned for tasks?
Yes, through prompt tuning or instruction tuning — optimizing prompts for specific downstream tasks without updating the model.
64. What is role-based prompting?
Assigning a role or persona (e.g., “You are a legal expert”) to guide the tone, style, and type of response the model gives.
65. Why is prompt engineering crucial in Generative AI applications?
It enables better control, efficiency, safety, and task performance from general-purpose models without retraining or fine-tuning.
Diffusion Models & GANs Interview Questions
66. What is a diffusion model in Generative AI?
A diffusion model generates data by gradually denoising random noise using a learned reverse process, often producing high-quality images.
67. What is DDPM (Denoising Diffusion Probabilistic Model)?
DDPM is a class of generative models where data is progressively corrupted and then reconstructed using a neural network-based denoising process.
68. How does Stable Diffusion work?
Stable Diffusion is a latent diffusion model that operates in a compressed latent space to generate images efficiently from text prompts.
69. What is UNet architecture in diffusion models?
UNet is a convolutional neural network with downsampling and upsampling paths, used for denoising steps in diffusion-based image generation.
70. What is the forward and reverse process in diffusion models?
The forward process adds noise to data step-by-step, while the reverse process learns to remove it and reconstruct the original sample.
71. What is a Variational Autoencoder (VAE)?
A VAE is a generative model that learns a probabilistic mapping from data to a latent space and reconstructs samples with a decoder.
72. What are GANs (Generative Adversarial Networks)?
GANs are a class of generative models where a generator creates fake data and a discriminator learns to distinguish real from fake.
73. What is the architecture of a GAN?
It consists of two networks: a Generator that produces fake data, and a Discriminator that evaluates its authenticity.
74. What is mode collapse in GANs?
Mode collapse occurs when the generator produces limited or identical outputs, failing to capture the diversity of the training data.
75. How can GAN training be stabilized?
Using techniques like feature matching, Wasserstein loss, gradient penalty, and spectral normalization.
76. What are conditional GANs (cGANs)?
cGANs generate data conditioned on additional inputs (e.g., class labels or text), allowing controlled content generation.
77. Compare GANs and diffusion models.
GANs are faster but harder to train; diffusion models are more stable, controllable, and produce higher-quality images.
78. What is a latent diffusion model?
A model that operates in the latent space (compressed representation) of an image instead of pixel space, reducing computational cost.
79. What are some real-world applications of diffusion models?
AI art (e.g., Midjourney), photorealistic editing, inpainting, drug discovery, and product design.
80. Why are diffusion models preferred over GANs in 2025?
Due to their better image quality, stability in training, and capability for fine-grained control via conditioning.
Fine-Tuning & Transfer Learning in Generative AI
81. What is fine-tuning in Generative AI?
Fine-tuning is adapting a pre-trained generative model (like GPT or Stable Diffusion) to a specific domain or task using new labeled or curated data.
82. Why is fine-tuning important for LLMs?
It enhances model performance on niche tasks, improves accuracy, and allows alignment with specific organizational or industry needs.
83. What is transfer learning in AI?
Transfer learning involves leveraging knowledge from a pre-trained model to solve a new but related task with less data and computation.
84. What’s the difference between fine-tuning and prompt engineering?
Prompt engineering guides behavior without altering model weights, while fine-tuning changes the model parameters for long-term adaptation.
85. What is LoRA (Low-Rank Adaptation)?
LoRA is a technique to fine-tune large models efficiently by injecting low-rank matrices into the model’s weights, reducing computational cost.
86. What is PEFT (Parameter-Efficient Fine-Tuning)?
PEFT refers to methods like LoRA, adapters, and prefix tuning that allow fine-tuning using fewer trainable parameters, saving resources.
87. What are adapter layers in Transformers?
Adapter layers are small, trainable layers inserted into a frozen Transformer that enable task-specific learning without modifying the core model.
88. What is instruction tuning?
A technique where a model is fine-tuned on datasets containing prompts and their corresponding responses to improve generalization to unseen instructions.
89. What is domain adaptation in LLMs?
It’s fine-tuning a language model on domain-specific corpora (e.g., legal, medical) so it performs better in that specific context.
90. What is prefix tuning?
A PEFT method where a fixed prompt is prepended to the input sequence during training, allowing task specialization without updating the entire model.
91. What is the risk of overfitting during fine-tuning?
Overfitting causes the model to memorize training data, reducing its ability to generalize to new inputs.
92. What is catastrophic forgetting in transfer learning?
When a fine-tuned model forgets its pre-trained knowledge while adapting to a new task, resulting in degraded performance on general tasks.
93. How can overfitting be prevented in fine-tuning?
Using techniques like dropout, regularization, early stopping, data augmentation, and using fewer fine-tuning steps.
94. What types of data are best for fine-tuning LLMs?
High-quality, task-specific, and diverse datasets, especially human-annotated examples, yield better fine-tuning outcomes.
95. What are some tools/libraries used for fine-tuning LLMs?
HuggingFace Transformers, DeepSpeed, LoRA (via PEFT), LangChain (for orchestration), and PyTorch Lightning.
RLHF & Model Alignment in Generative AI
96. What is RLHF (Reinforcement Learning with Human Feedback)?
RLHF is a training technique where a language model is fine-tuned using reward signals derived from human preferences, improving its alignment with human values.
97. What are the three main steps in RLHF?
- Supervised Fine-Tuning (SFT) using human-annotated responses
- Reward Model Training using human comparisons
- Policy Optimization using reinforcement learning (typically PPO)
98. What is PPO (Proximal Policy Optimization)?
PPO is a stable and efficient reinforcement learning algorithm used in RLHF to optimize the model’s responses with respect to the reward model.
99. Why is RLHF important for Generative AI models?
It helps reduce harmful, biased, or toxic outputs and aligns models more closely with human intent and ethical expectations.
100. What is a reward model in RLHF?
A neural network trained to assign a preference score to generated outputs, based on human feedback (e.g., ranking of responses).
101. What are the challenges of RLHF?
It’s expensive, relies heavily on human labor, can suffer from reward hacking, and may introduce biases from annotators.
102. What is model alignment?
Ensuring a generative model’s behavior aligns with human intentions, goals, values, and safety requirements.
103. What is preference modeling in AI?
It involves learning from user feedback to model subjective preferences between different outputs.
104. How is RLHF used in ChatGPT and GPT-4?
OpenAI uses RLHF to make ChatGPT more helpful, safe, and less likely to produce undesirable or biased responses.
105. What are alternatives to RLHF?
Constitutional AI (Anthropic), direct preference optimization (DPO), supervised fine-tuning with larger datasets, and automated feedback systems.
Evaluation Metrics & Model Performance in Generative AI
106. Why is evaluation important in Generative AI?
Evaluation helps assess output quality, relevance, coherence, fluency, accuracy, and safety — ensuring models meet real-world requirements.
107. What are BLEU and ROUGE scores used for?
- BLEU (Bilingual Evaluation Understudy) measures precision in text generation by comparing n-grams to reference texts.
- ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measures recall and overlap of words between generated and reference summaries.
108. What is METEOR?
METEOR (Metric for Evaluation of Translation with Explicit ORdering) evaluates generated text based on synonymy, stemming, and word order — offering better alignment with human judgment than BLEU.
109. What is perplexity in language models?
Perplexity measures how well a language model predicts a sample. Lower perplexity indicates better prediction (i.e., the model is less “surprised”).
110. What is FID (Fréchet Inception Distance)?
FID evaluates the quality of generated images by comparing distributions of real and fake image embeddings using an Inception model. Lower FID = better image quality.
111. What is Inception Score (IS)?
IS evaluates image quality and diversity based on how confidently a classifier (InceptionNet) can predict a single class and how evenly predictions are spread across images.
112. What is CLIPScore?
CLIPScore measures the alignment between text prompts and generated images using CLIP embeddings. Higher scores indicate better text-image alignment.
113. How is hallucination measured in LLMs?
Through human evaluation, factual consistency checks (e.g., fact-checking APIs), or automated benchmarks like TruthfulQA and FEVER.
114. What are human evaluation methods in Generative AI?
Manual scoring of output for helpfulness, factuality, fluency, and safety — often using Likert scales or pairwise comparison.
115. What tools are used for evaluating generative outputs?
- Text: NLG Eval, SacreBLEU, ROUGE Toolkit
- Vision: FID, IS, CLIPScore
- Audio: PESQ, STOI
- Multimodal: LAVIS, GQA, VQAv2
MLOps & LLMOps in Generative AI
116. What is MLOps?
MLOps (Machine Learning Operations) is the practice of automating and streamlining the deployment, monitoring, and lifecycle management of ML models in production.
117. What is LLMOps?
LLMOps is a specialized subfield of MLOps focused on large language models, covering versioning, serving, cost optimization, prompt orchestration, and hallucination monitoring.
118. How is deploying a generative model different from a standard ML model?
Generative models require larger compute, dynamic memory handling, real-time inference capabilities, and guardrails for hallucinations and unsafe outputs.
119. What is model serving in LLMOps?
Model serving is hosting an LLM (like GPT-J, Falcon, or LLaMA) on inference endpoints using frameworks like TorchServe, Triton, or Hugging Face Inference Endpoints.
120. What is LangChain used for?
LangChain is a framework for building LLM-powered apps with tools for chaining prompts, integrating vector databases, and managing context windows efficiently.
121. What are common deployment platforms for LLMs?
AWS Sagemaker, Azure ML, Vertex AI, Hugging Face Spaces, Replicate, BentoML, Modal, and custom Docker + Kubernetes stacks.
122. What is prompt orchestration?
Managing complex chains of prompts dynamically across user sessions, tools, and external APIs to enable multi-step reasoning in LLM apps.
123. What is model monitoring in production?
Tracking performance, latency, output drift, token usage, hallucination rates, and safety triggers in real-time for deployed models.
124. What are vector databases and how are they used?
Databases like Pinecone, FAISS, and Weaviate store embeddings of documents to enable semantic search and retrieval-augmented generation (RAG).
125. How can you optimize LLM cost in production?
- Use quantization (e.g., 8-bit)
- Serve smaller models for simple tasks
- Cache responses
- Use prompt compression or truncation
- Leverage open-source models
Vision & Multimodal Generative AI
126. What is multimodal Generative AI?
Multimodal Generative AI can process and generate content across different modalities—such as text, images, audio, and video—either independently or in combination.
127. What are some popular multimodal models?
CLIP, Flamingo, GPT-4 (Multimodal), DALL·E 3, Gemini (Google), Kosmos-1, and LLaVA are widely used multimodal models.
128. What is CLIP and how is it used?
CLIP (Contrastive Language-Image Pretraining) jointly trains on image-text pairs to match text with relevant images and is often used for image-text alignment and scoring.
129. What is DALL·E and how does it work?
DALL·E is a generative model that creates images from text prompts using a transformer architecture trained on image-caption datasets.
130. What is the architecture behind Stable Diffusion?
Stable Diffusion uses a latent diffusion model with a UNet denoiser, a VAE encoder/decoder, and CLIP for text-to-image guidance.
131. What is image captioning in Generative AI?
It’s the process of generating a textual description of an image using encoder-decoder architectures or multimodal transformers.
132. What are vision transformers (ViTs)?
ViTs apply the Transformer architecture directly to image patches, allowing self-attention-based learning for vision tasks.
133. What is cross-attention in multimodal models?
Cross-attention lets one modality (e.g., text) attend to another (e.g., image) to generate contextually coherent outputs across modalities.
134. What are the challenges in training multimodal models?
Challenges include data alignment, modality imbalance, high computational cost, and semantic misalignment between text and vision.
135. What are some use cases for multimodal AI in 2025?
Text-to-image generation, video summarization, AR/VR content creation, autonomous vehicles, voice-to-3D modeling, and AI design tools.
136. What are the main ethical concerns in Generative AI?
Key concerns include misinformation, deepfakes, bias, toxicity, misuse of personal data, copyright infringement, and AI hallucinations.
137. What is model hallucination?
It’s when a generative model produces factually incorrect or fabricated content that appears plausible or authoritative.
138. How can bias arise in Generative AI models?
Bias arises from imbalanced or prejudiced training data, flawed annotations, or feedback loops, leading to discriminatory or harmful outputs.
139. What is Responsible AI in the context of Generative AI?
Responsible AI ensures models are developed and deployed ethically, safely, transparently, and with accountability to mitigate social harm.
140. How can hallucinations in LLMs be reduced?
Using techniques like retrieval-augmented generation (RAG), fine-tuning with verified data, prompt engineering, and real-time fact-checking.
141. What is AI watermarking?
AI watermarking embeds identifiable signals into generated content to track, authenticate, or trace AI-generated outputs (e.g., for copyright or governance).
142. What is the EU AI Act?
A regulatory framework classifying AI systems by risk and requiring transparency, explainability, and governance controls — especially for generative models.
143. Are outputs from Generative AI copyrighted?
In most jurisdictions as of 2025, AI-generated content without human creative input is not eligible for copyright protection.
144. What is explainability in Generative AI?
Explainability refers to the ability to interpret and understand how and why an AI system produces a specific output.
145. What governance strategies are used for Generative AI?
Governance includes model auditing, usage policies, red-teaming, human-in-the-loop oversight, API rate limits, and ethical review boards.
