A. Basics of Generative AI
1. What is Generative AI?
Generative AI is a type of artificial intelligence that can create new content such as text, images, audio, or code based on the data it has learned from. It goes beyond classification or prediction to generate novel outputs.
2. How does Generative AI differ from traditional AI?
Traditional AI often focuses on predictions or classifications, while Generative AI produces new content that did not exist in the original dataset. For example, it can write an article or create an image.
3. What are some popular examples of Generative AI?
Examples include ChatGPT for text, DALL·E for images, GitHub Copilot for code, and MusicLM for audio generation. These systems demonstrate AI creativity across different modalities.
4. What is the role of a language model in Generative AI?
Language models predict the next word or token in a sequence based on context, enabling them to generate coherent text. They are the foundation for most text-based generative AI applications.
5. What are some types of Generative AI?
Common types include Large Language Models (LLMs), Diffusion Models for images, GANs (Generative Adversarial Networks), and Variational Autoencoders (VAEs). Each has unique strengths and use cases.
B. Technical / Model-Specific Questions
6. What is a Transformer model?
Transformers are deep learning architectures that use attention mechanisms to process sequential data efficiently. They are the backbone of modern LLMs like GPT and BERT.
7. What is attention in Transformers?
Attention allows the model to focus on important parts of the input when generating outputs. It calculates a weighted importance score for each token in the input sequence.
8. What is the difference between GPT and BERT?
GPT is generative and predicts the next token to create content, while BERT is bidirectional and excels at understanding context in both directions, mainly for classification tasks.
9. What is a Generative Adversarial Network (GAN)?
A GAN consists of a generator that creates new data and a discriminator that evaluates its authenticity. The two networks compete, improving each other over time.
10. Explain Diffusion Models in Generative AI.
Diffusion models generate data by gradually removing noise from random inputs until a meaningful output emerges. They are widely used for high-quality image synthesis.
11. What is the purpose of fine-tuning in Generative AI?
Fine-tuning adapts a pre-trained model to a specific task or domain, improving accuracy and relevance. It allows the model to specialize without retraining from scratch.
12. What is prompt engineering?
Prompt engineering is the practice of designing input prompts that guide a Generative AI model to produce desired outputs. It’s crucial for improving response quality.
13. What is zero-shot and few-shot learning?
Zero-shot learning generates outputs without prior examples for the task, while few-shot learning uses a small number of examples to guide the model’s behavior. Both reduce training data requirements.
14. What is tokenization in LLMs?
Tokenization breaks text into smaller units (tokens) such as words, subwords, or characters. Models process these tokens instead of raw text to generate predictions.
15. What is model hallucination in Generative AI?
Hallucination occurs when a model generates incorrect or nonsensical outputs that appear plausible. Managing hallucinations is a key challenge in deploying LLMs.
C. Data & Training Questions
16. What kind of data is used to train Generative AI models?
Models are trained on large datasets including text, images, code, or audio. The data is usually diverse, high-quality, and preprocessed to improve learning.
17. What is data preprocessing in Generative AI?
Preprocessing includes cleaning, normalization, tokenization, and sometimes augmentation. Proper preprocessing ensures better model performance and reduces errors.
18. What is overfitting in Generative AI?
Overfitting happens when a model learns training data too well and fails to generalize to new data. Regularization and data augmentation help prevent overfitting.
19. How is model evaluation done in Generative AI?
Evaluation can involve quantitative metrics like BLEU, ROUGE, or FID, as well as qualitative human evaluation to assess coherence, creativity, and relevance of outputs.
20. What is transfer learning in Generative AI?
Transfer learning leverages pre-trained models and adapts them to new tasks with minimal training data. It accelerates development and reduces computational costs.
21. What are embeddings in Generative AI?
Embeddings are vector representations of data (text, image, or audio) that capture semantic meaning. They allow models to understand similarities and relationships between inputs.
22. How do LLMs handle long text sequences?
LLMs use attention mechanisms and positional encoding to manage dependencies across long sequences. Recent architectures also introduce techniques like sparse attention to handle extended contexts.
23. What is reinforcement learning with human feedback (RLHF)?
RLHF trains models using human feedback on outputs to improve alignment with human preferences. It’s commonly used to reduce harmful or irrelevant outputs in LLMs.
24. How do you manage bias in Generative AI models?
Bias is mitigated by using diverse, high-quality datasets, fairness-aware training, and post-processing. Continuous monitoring and evaluation are essential to minimize harm.
25. What is the difference between supervised and unsupervised training in Generative AI?
Supervised training uses labeled data for prediction, while unsupervised training learns patterns without explicit labels. Generative models often use unsupervised or self-supervised learning.
D. Use Cases & Applications
26. Name some text-based Generative AI applications.
Applications include chatbots, content creation, summarization, translation, and code generation. ChatGPT and GitHub Copilot are prime examples.
27. Name some image-based Generative AI applications.
Applications include AI art, product design, style transfer, and image enhancement. DALL·E and Stable Diffusion are widely used models.
28. How is Generative AI used in healthcare?
It can generate synthetic medical data, assist in diagnosis, and support personalized treatment plans. This improves research while maintaining patient privacy.
29. How is Generative AI used in marketing?
Marketers use it to create content, personalized campaigns, ad copy, and social media posts. It accelerates content creation and improves engagement.
30. How can Generative AI generate code?
It predicts the next token in programming languages based on context, completing or suggesting code snippets. GitHub Copilot and ChatGPT are practical examples.
31. How is AI used in text summarization?
Generative AI condenses long documents into concise summaries by understanding context, key points, and relationships within the text.
32. Can Generative AI create realistic synthetic data?
Yes, it can generate synthetic datasets for training other AI models while protecting sensitive or private information.
33. How is Generative AI applied in gaming?
It can generate game levels, characters, dialogue, and scenarios dynamically, reducing manual content creation and enhancing player experience.
34. What are multimodal Generative AI applications?
These models can process and generate multiple data types, such as generating images from text or combining audio, video, and text for rich outputs.
35. How is AI used in chatbots?
Chatbots leverage LLMs to understand user input and generate human-like responses, enabling customer service, tutoring, or interactive entertainment.
E. System Design & Deployment
36. How do you deploy a Generative AI model in production?
Deployment involves serving the model through APIs, scaling with cloud infrastructure, monitoring performance, and ensuring security and reliability.
37. What is latency in AI deployment?
Latency is the time it takes for a model to process input and produce output. Low latency is critical for real-time applications like chatbots.
38. What are challenges in scaling Generative AI systems?
Challenges include compute costs, model size, inference speed, and memory requirements. Efficient model architecture and cloud optimization help overcome them.
39. How do you handle updates to a deployed model?
Models are updated through incremental retraining, fine-tuning, or versioning. Monitoring performance and backward compatibility is essential during updates.
40. How do you secure Generative AI models?
Security involves API authentication, encryption, monitoring for adversarial inputs, and restricting sensitive data usage during inference.
F. Ethics, Bias & Safety
41. What are ethical concerns in Generative AI?
Concerns include misinformation, deepfakes, copyright violations, bias, and unintended harmful content. Developers must consider responsible use and safeguards.
42. How do you reduce bias in Generative AI outputs?
Bias reduction involves curating diverse datasets, using fairness algorithms, evaluating outputs for harmful patterns, and implementing feedback loops.
43. What is AI hallucination and why is it a problem?
Hallucination is when AI generates factually incorrect or misleading outputs. It poses risks in critical applications like healthcare, finance, or legal domains.
44. How do you prevent misuse of Generative AI?
Preventive measures include usage policies, monitoring, content filtering, watermarking outputs, and limiting access to powerful models.
45. What is model explainability in Generative AI?
Explainability involves understanding why a model generates certain outputs. It improves trust, helps debug errors, and ensures accountability.
G. Advanced Topics
46. What are embeddings in multimodal Generative AI?
Embeddings represent data from different modalities (text, image, audio) in a common vector space, enabling cross-modal generation and retrieval.
47. What is retrieval-augmented generation (RAG)?
RAG combines LLMs with external knowledge sources to generate accurate and contextually grounded outputs. It reduces hallucinations by leveraging real data.
48. How do you evaluate creativity in Generative AI?
Creativity is evaluated qualitatively through human assessment and quantitatively using diversity, novelty, and uniqueness metrics in outputs.
49. What are AI agents?
AI agents are autonomous systems powered by generative models that can perform tasks, make decisions, and interact with the environment without constant human input.
50. What are some challenges in multimodal Generative AI?
Challenges include aligning different modalities, managing high computational costs, ensuring consistency, and preventing errors like mismatched outputs.
51. How is few-shot prompting different from fine-tuning?
Few-shot prompting provides examples in the prompt at inference time, whereas fine-tuning updates the model weights for a specific task. Both aim to improve performance on specialized tasks.
Conclusion
Generative AI is no longer just a buzzword it’s a rapidly evolving field shaping industries from healthcare and marketing to software development and creative arts. As this technology grows, so does the demand for professionals who understand not only how these models work, but also their ethical implications, deployment challenges, and practical applications.
Preparing for a Generative AI interview requires a mix of technical knowledge, practical experience, and awareness of real-world use cases. By familiarizing yourself with common interview questions covering everything from transformers and GANs to prompt engineering and bias mitigation you’ll be well-equipped to demonstrate both your expertise and strategic thinking.
Ultimately, mastery of Generative AI isn’t just about memorizing answers; it’s about understanding the principles behind the models, their capabilities, and their limitations. With the right preparation, you can confidently showcase your skills and contribute meaningfully to this transformative field.
- Explore Jeevi’s Page for detailed resources.
