#GenAI

Last updated    - V4 (13-Sep-2025)

Level Topic Subtopics Basic Generative AI What is Generative AI, History of Generative AI, Applications, Difference from Discriminative Models, Overview of Generative AI Models AI & ML Fundamentals Basics of Machine Learning, Neural Networks, Supervised vs Unsupervised Learning, Reinforcement Learning, Probability & Statistics for Generative Models Data Preparation Data Collection, Data Cleaning, Feature Engineering, Dataset Splits, Evaluation Metrics Basics Tools & Frameworks Python, TensorFlow, PyTorch, Hugging Face Transformers, OpenAI APIs, Google Colab Ethics & Safety Bias in Generative Models, Deepfakes, Misinformation, Responsible AI, Copyright Considerations Intermediate Generative Models Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), Conditional GANs, Diffusion Models, Flow-based Models Text Generation Language Models, GPT Architecture, Tokenization, Text Preprocessing, Prompt Engineering Image Generation Convolutional Networks, Image-to-Image Translation, Style Transfer, Image Augmentation, Pretrained Models Model Training Loss Functions, Optimization, Gradient Descent, Regularization, Training Stability Evaluation & Metrics Perplexity, FID Score, Inception Score, BLEU Score, Human Evaluation Advanced Advanced Generative Architectures Transformers, Attention Mechanism, Diffusion Models, Large Language Models (LLMs), Multi-modal Models Fine-Tuning & Adaptation Transfer Learning, Domain Adaptation, Parameter Efficient Fine-Tuning (PEFT), LoRA, Prompt Tuning Multi-Modal Generative AI Text-to-Image, Text-to-Audio, Text-to-Video, Cross-Modal Retrieval, Embedding Spaces Model Deployment & Scaling Serving Models, APIs, Latency Optimization, Distributed Training, Cloud Deployment Security & Robustness Adversarial Attacks, Model Poisoning, Hallucinations, Bias Mitigation, Model Auditing Expert State-of-the-Art Generative AI GPT-4/5, DALL-E, Stable Diffusion, MidJourney, Open-Source LLMs, Advanced Diffusion Techniques Research & Innovation Self-Supervised Learning, Few-Shot & Zero-Shot Learning, Reinforcement Learning with Human Feedback (RLHF), AI Alignment, Generative Model Evaluation Research Explainability & Interpretability SHAP, LIME, Counterfactual Explanations, Understanding Latent Spaces, Debugging Generative Models Ethics, Governance & Policy Regulation of Generative AI, Deepfake Detection, Intellectual Property, Privacy-Preserving AI, Responsible Deployment Strategies Performance & Optimization Mixed Precision Training, Memory Optimization, Inference Acceleration, Efficient Architectures, Energy-Efficient AI

1. Generative AI Basics

1. What is Generative AI and how does it differ from discriminative AI?
2. Explain common applications of Generative AI.
3. How did Generative AI evolve over the years?
4. Difference between supervised, unsupervised, and generative learning.
5. Explain the concept of latent space in Generative AI.
6. What are some real-world use cases of Generative AI?
7. Explain the difference between deterministic and probabilistic models.
8. What is the role of probability and statistics in Generative AI?
9. Explain tokenization in NLP generative models.
10. Difference between text generation, image generation, and multi-modal generation.
11. What are ethical concerns in Generative AI?
12. How does copyright affect AI-generated content?
13. Explain model hallucinations in Generative AI.
14. How do you evaluate a generative model qualitatively?
15. Difference between narrow AI and generative AI capabilities.
16. What is overfitting in Generative AI models?
17. Explain underfitting in generative models.
18. How do generative models learn patterns from data?
19. Difference between pretraining and fine-tuning in Generative AI.
20. What is prompt engineering in text generation?
21. How do generative AI tools handle user data?
22. Explain the role of embeddings in generative models.
23. What is the difference between shallow and deep generative models?
24. How do you preprocess data for Generative AI models?
25. Explain evaluation metrics like BLEU and FID.

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2. Intermediate Generative Models

1. Explain Variational Autoencoders (VAE).
2. What is a Generative Adversarial Network (GAN)?
3. Difference between GAN and VAE.
4. Explain Conditional GANs and their use cases.
5. How do Diffusion Models work?
6. What is Flow-based generative modeling?
7. How do you implement tokenization for GPT-style models?
8. Explain embedding representations in generative text models.
9. Difference between RNN-based and Transformer-based generative models.
10. How do you perform sequence generation?
11. Explain beam search in text generation.
12. What is temperature in text generation models?
13. Explain the role of attention mechanism in generative models.
14. How do you fine-tune a pre-trained generative model?
15. Difference between supervised fine-tuning and reinforcement fine-tuning.
16. What are prompt-based models?
17. Explain training instability in GANs.
18. How do you prevent mode collapse in GANs?
19. How do you handle overfitting in VAEs?
20. Difference between text-to-text and text-to-image generation.
21. How do you evaluate generative models quantitatively?
22. Explain Inception Score (IS) and Fréchet Inception Distance (FID).
23. How do you handle multi-modal generative tasks?
24. Explain the importance of data augmentation in generative training.
25. What are the limitations of intermediate generative models?

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3. Advanced Generative AI

1. Explain transformer architecture for generative AI.
2. How does self-attention work in transformers?
3. What is GPT architecture and its key components?
4. Difference between GPT and BERT.
5. Explain large language models (LLMs).
6. How do diffusion models generate high-quality images?
7. Explain latent diffusion models.
8. What is fine-tuning with LoRA (Low-Rank Adaptation)?
9. How do you perform parameter-efficient fine-tuning (PEFT)?
10. Difference between few-shot, zero-shot, and one-shot learning.
11. Explain reinforcement learning with human feedback (RLHF).
12. How do generative models handle multi-turn conversations?
13. Explain text-to-image models like DALL-E or Stable Diffusion.
14. What are attention-based cross-modal models?
15. Explain embedding spaces for multi-modal AI.
16. How do you optimize generative AI for inference speed?
17. Explain memory-efficient training techniques.
18. What are common failure modes in advanced generative models?
19. How do you handle hallucinations in generative outputs?
20. Explain evaluation metrics for advanced generative AI.
21. How do you monitor and log generative model performance?
22. Explain model distillation for large generative models.
23. How do you perform continuous learning in generative AI?
24. Explain token mixing and positional encoding.
25. How do you deploy generative models in production?

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4. Expert-Level Generative AI

1. Explain GAN variants (StyleGAN, CycleGAN, BigGAN).
2. How do advanced diffusion models work?
3. Explain text-to-video generative models.
4. How do generative AI models scale to billions of parameters?
5. Explain multimodal foundation models.
6. How do you implement RLHF at scale?
7. Explain alignment challenges in generative AI.
8. What is AI interpretability for generative models?
9. How do you use SHAP or LIME for generative models?
10. Explain model auditing and debugging in production.
11. How do you detect and mitigate bias in generative AI?
12. Explain privacy-preserving generative AI.
13. How do you handle copyright and licensing issues for generated content?
14. Explain federated learning for generative AI.
15. How do you evaluate generative AI in open-ended tasks?
16. Explain multimodal embeddings and cross-modal retrieval.
17. How do you combine diffusion and transformer architectures?
18. Explain advanced tokenization strategies for large models.
19. How do you optimize large-scale training costs?
20. Explain energy-efficient architectures for generative AI.
21. How do you implement AI alignment techniques?
22. How do you monitor hallucinations in deployed LLMs?
23. Explain safety measures for generative AI in production.
24. How do you research state-of-the-art generative AI techniques?
25. What are future trends in generative AI research and deployment?