![Generative AI Interview Questions and Answers]()
1. What is Generative AI?
Generative AI refers to AI systems that can generate text, images, audio, video, or other content based on input data or prompts, using models like GPT, DALL·E, and others.
If you're new to the concept or want a deeper dive into how Generative AI works, its benefits, and real-world applications, check out this detailed article: What is Generative AI.
2. How does Generative AI differ from traditional AI?
Traditional AI often focuses on prediction or classification, whereas Generative AI focuses on content creation, mimicking human-like output from patterns in data.
3. Name some popular Generative AI models.
Popular models include OpenAI’s GPT-4, DALL·E, Midjourney, Stability AI’s Stable Diffusion, Meta’s LLaMA, Google’s Gemini, and Anthropic’s Claude.
4. What is a Transformer in AI?
Transformer is a deep learning architecture introduced by Google in 2017. It is the foundation of most large language models and uses self-attention mechanisms for processing data.
5. Explain the difference between GPT and BERT.
GPT is generative and autoregressive, trained to predict the next word in a sentence. BERT is bidirectional and trained for tasks like classification and question answering.
6. What is the role of the attention mechanism in Transformers?
The attention mechanism allows models to focus on relevant parts of the input, enabling better understanding of context and relationships in sequences.
7. How does ChatGPT work?
ChatGPT is based on the GPT architecture and generates responses by predicting the most likely next words based on the prompt using deep learning.
8. What is the difference between training and fine-tuning?
Training involves learning from scratch, while fine-tuning adjusts a pre-trained model to suit specific tasks or datasets.
9. What is prompt engineering?
Prompt engineering involves crafting effective prompts to guide Generative AI models to produce desired outputs efficiently and accurately.
10. What are hallucinations in Generative AI?
Hallucinations occur when AI models generate plausible-sounding but incorrect or made-up information.
11. What is tokenization in NLP?
Tokenization is the process of breaking text into smaller pieces (tokens), such as words or subwords, for processing by NLP models.
12. How does Generative AI handle multilingual content?
Modern models are trained on multilingual corpora, allowing them to understand and generate content in multiple languages effectively.
13. What are diffusion models in Generative AI?
Diffusion models generate data by gradually removing noise from a random input, widely used in image generation tools like Stable Diffusion.
14. What is Reinforcement Learning from Human Feedback (RLHF)?
RLHF is a technique where human feedback is used to fine-tune AI models to produce more helpful and aligned responses.
15. What are the limitations of Generative AI?
Limitations include hallucinations, bias in training data, lack of reasoning, data privacy risks, and high compute requirements.
16. How can bias be mitigated in Generative AI models?
Bias mitigation involves curating diverse datasets, using fairness constraints, and applying post-processing or debiasing algorithms.
17. What are the ethical concerns with Generative AI?
Concerns include misinformation, deepfakes, intellectual property misuse, and reduced human agency in decision-making.
18. What is zero-shot and few-shot learning?
Zero-shot learning requires no examples; few-shot uses a few examples to guide the model to perform a task it wasn’t explicitly trained for.
19. How do LLMs handle long context inputs?
LLMs use positional embeddings and windowed attention to manage longer sequences, though some models struggle with long memory.
20. What is the difference between generative and discriminative models?
Generative models generate new data; discriminative models classify or predict labels based on existing data.
21. What is a language model?
A language model predicts the likelihood of a sequence of words and is foundational to many NLP and generative applications.
22. What is latent space in AI?
Latent space is a compressed representation of features learned by AI models, used especially in models like autoencoders and VAEs.
23. What is overfitting in AI models?
Overfitting occurs when a model learns the training data too well, including noise, and performs poorly on unseen data.
24. What are embeddings?
Embeddings are vector representations of words, sentences, or other inputs that capture semantic relationships for processing by models.
25. How is Generative AI used in healthcare?
It’s used for synthetic data generation, medical imaging analysis, report generation, drug discovery, and virtual assistants.
26. What is the role of GANs in Generative AI?
GANs (Generative Adversarial Networks) consist of a generator and a discriminator, often used to generate realistic images or data.
27. How is Generative AI applied in finance?
It’s used for report generation, risk modeling, fraud detection, and simulating market scenarios using synthetic data.
28. What is a diffusion model?
A diffusion model is a generative model that learns to reverse a noise process to generate high-quality samples, often used in image generation.
29. What is an autoregressive model?
An autoregressive model generates sequences where each output depends on the previous elements in the sequence.
30. What is temperature in text generation?
Temperature controls the randomness of predictions. Lower values make output more deterministic, higher values increase creativity.
31. What is top-k sampling in Generative AI?
Top-k sampling limits predictions to the top k probable next tokens, promoting diversity while controlling randomness.
32. What is top-p (nucleus) sampling?
Top-p sampling considers the smallest set of top probable tokens whose cumulative probability is at least p, adding more flexibility than top-k.
33. What is the role of Generative AI in creative industries?
It supports art, music, writing, and design by automating creation, enhancing creativity, and accelerating workflows.
34. What are some use cases of Generative AI in marketing?
Use cases include ad copy generation, customer segmentation, campaign automation, and personalized content generation.
35. How does Generative AI impact education?
It provides tutoring, essay assistance, quiz generation, personalized feedback, and content adaptation based on learner needs.
36. What is chain-of-thought reasoning?
Chain-of-thought reasoning involves step-by-step logical processing in AI models to improve reasoning in tasks like math and logic.
37. What is multi-modal Generative AI?
Multi-modal Generative AI processes and generates across different input types—like combining text, images, audio, and video.
38. How does Generative AI assist in code generation?
It helps developers by generating code snippets, completing code, refactoring, and even detecting bugs using natural language input.
39. What are token limits in LLMs?
Token limits define the maximum length of input/output sequences a model can process; exceeding them truncates or causes failure.
40. What is model hallucination?
Model hallucination refers to incorrect or fabricated output generated by the model that appears plausible but is false or misleading.
41. How is Generative AI regulated globally?
Regulations vary by region; the EU AI Act and policies from the US, India, and China aim to ensure responsible and ethical use.
42. What is parameter tuning?
Parameter tuning involves adjusting model hyperparameters like learning rate, batch size, or temperature to optimize performance.
43. What is LoRA (Low-Rank Adaptation)?
LoRA is a fine-tuning technique for LLMs that reduces the number of parameters being trained, saving memory and compute resources.
44. How is Generative AI integrated into mobile apps?
It’s integrated using on-device inference, APIs, or cloud-based models to enable features like chatbots, image editing, and voice synthesis.
45. What is context window in LLMs?
It’s the maximum length of input the model can consider at once. GPT-4, for instance, can handle up to 128k tokens depending on the version.
46. What is the role of Generative AI in cybersecurity?
It assists in threat modeling, simulating attack patterns, anomaly detection, and automating incident response.
47. How do AI models ensure factual consistency?
Through retrieval-augmented generation (RAG), grounding in trusted sources, and reinforcement learning using factual datasets.
48. What is prompt chaining?
Prompt chaining links multiple prompts and responses in a sequence to complete complex multi-step tasks in Generative AI applications.
49. What is the impact of Generative AI on jobs?
It automates routine tasks but also creates new roles in AI oversight, prompt engineering, ethics, and creativity augmentation.
50. What are some future trends in Generative AI?
Trends include agentic AI, AI copilots, domain-specific LLMs, energy-efficient training, synthetic data use, and tighter regulation.
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