#GenAI

Last updated    - V7 (09-Jan-2026)

Key Concepts

S.No	Topic	Sub-Topics
1	Introduction to Generative AI	History of AI, Overview of Generative AI, Types of Generative Models, Key Use Cases, AI vs Human Creativity
2	AI Fundamentals	ML Basics, DL Overview, Neural Networks, Supervised vs Unsupervised Learning, Activation Functions
3	Generative Models Overview	Definition, Types, Applications, Strengths & Limitations, Popular Frameworks
4	Probabilistic Models	Bayesian Networks, Markov Chains, Hidden Markov Models, Conditional Probability, Inference Techniques
5	Variational Autoencoders (VAE)	Architecture, Encoder & Decoder, Latent Space, Loss Functions, Applications
6	Generative Adversarial Networks (GANs)	Generator & Discriminator, Training Process, Loss Functions, Popular Variants (DCGAN, CycleGAN), Applications
7	Diffusion Models	Concept of Diffusion, Forward & Reverse Processes, Noise Schedules, Denoising, Use Cases in Images
8	Transformers for GenAI	Attention Mechanism, Encoder-Decoder Architecture, Self-Attention, Positional Encoding, Use in Text/Image Generation
9	Large Language Models (LLMs)	Definition, Examples (GPT, BERT), Training Data, Tokenization, Limitations & Bias
10	Text Generation	Prompt Engineering, Beam Search, Temperature & Top-k Sampling, Fine-tuning Models, Text Summarization
11	Image Generation	GAN-based Image Generation, Diffusion Models, Text-to-Image, Inpainting, Super-Resolution
12	Audio & Music Generation	WaveNet, MusicVAE, Text-to-Speech, Voice Cloning, Sound Effects Generation
13	Video Generation	Video Synthesis, Frame Interpolation, Video-to-Video Translation, DeepFake Techniques, Ethical Considerations
14	Reinforcement Learning for GenAI	RL Basics, Policy & Value Networks, Reward Functions, RLHF (Human Feedback), Applications in Text & Games
15	Data Preparation & Augmentation	Dataset Collection, Cleaning, Normalization, Synthetic Data Generation, Data Labeling
16	Fine-Tuning & Transfer Learning	Pre-trained Models, Domain Adaptation, Hyperparameter Tuning, Prompt Tuning, LoRA & PEFT
17	Evaluation Metrics	Perplexity, BLEU, FID, IS, Human Evaluation, Metrics for Text, Image, Audio
18	Ethics in GenAI	Bias & Fairness, DeepFakes, Misinformation, Copyright Issues, Responsible AI Guidelines
19	GenAI Frameworks & Tools	TensorFlow, PyTorch, HuggingFace Transformers, Diffusers, OpenAI API
20	Prompt Engineering	Effective Prompts, Chain-of-Thought, Zero-shot vs Few-shot, Context Management, Prompt Templates
21	GenAI in NLP Applications	Chatbots, Summarization, Translation, Sentiment Analysis, Question Answering
22	GenAI in Vision Applications	Art Generation, Style Transfer, Image Enhancement, Object Synthesis, Medical Imaging
23	GenAI in Audio Applications	Music Composition, Voice Cloning, Speech-to-Text, Soundscapes, Podcast Generation
24	GenAI in Gaming & Simulation	NPC Dialogue, Procedural Content Generation, Story Generation, Game Level Design, AI Opponents
25	Deployment of GenAI Models	Cloud Services, API Deployment, Model Serving, Scalability, Monitoring
26	Optimization & Efficiency	Quantization, Pruning, Knowledge Distillation, Low-resource Training, Latency Reduction
27	GenAI for Business	Marketing Content, Personalized Recommendations, Automated Reports, Customer Support, Generative Analytics
28	Security & Privacy	Data Privacy, Model Stealing, Adversarial Attacks, Secure Model Sharing, Differential Privacy
29	Future Trends in GenAI	Multi-modal AI, Self-supervised Learning, General AI, AI Agents, AI in Healthcare & Research
30	Capstone Project	Choose a GenAI Domain, Collect Data, Train Model, Evaluate, Deploy, Document & Present

Interview question

📘 Basic Level

1. What is Generative AI (GenAI)?
2. What are the key features of GenAI?
3. How is GenAI different from traditional AI?
4. What are some common use cases of GenAI?
5. What is a generative model?
6. What are GANs (Generative Adversarial Networks)?
7. What is a Variational Autoencoder (VAE)?
8. What are diffusion models in GenAI?
9. What is an autoregressive model?
10. What is the role of a generator in GANs?
11. What is the role of a discriminator in GANs?
12. What is latent space in VAEs?
13. What is the difference between supervised and unsupervised GenAI models?
14. What is tokenization in NLP-based GenAI?
15. What is a transformer?
16. What are attention and self-attention mechanisms?
17. What are Large Language Models (LLMs)?
18. What is the context window in LLMs?
19. What is prompt engineering?
20. What is zero-shot learning?
21. What is few-shot learning?
22. What is the difference between generative and discriminative models?
23. What is text generation?
24. What is image generation using AI?
25. What are some common ethical concerns in GenAI?

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📗 Intermediate Level

1. How do GANs generate realistic data?
2. What is KL Divergence in VAEs?
3. What is the training process of a GAN?
4. How do diffusion models generate images?
5. What is the difference between DALL-E and Stable Diffusion?
6. What is inpainting in image generation?
7. How does reinforcement learning fit into GenAI?
8. What is RLHF (Reinforcement Learning with Human Feedback)?
9. What is the temperature parameter in text generation?
10. What are top-k and top-p sampling strategies?
11. How do transformers improve NLP tasks?
12. What is BERT and its use case?
13. What is GPT and how does it work?
14. How do embeddings work in text generation?
15. What is multimodal GenAI?
16. What is text-to-image generation?
17. What is text-to-audio generation?
18. How is synthetic data used in GenAI training?
19. What is prompt tuning?
20. How do you prevent hallucinations in LLMs?
21. How is human-in-the-loop used in GenAI?
22. How do you evaluate GenAI models?
23. What is FID (Fréchet Inception Distance)?
24. What is BLEU score for text evaluation?
25. What are common safety measures in GenAI applications?

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📕 Advanced Level

1. How do GANs and VAEs differ in their approach?
2. What is a conditional GAN?
3. How does StyleGAN work?
4. What is a diffusion denoising process?
5. How do you fine-tune large language models?
6. What is parameter-efficient fine-tuning (PEFT)?
7. What is LoRA in GenAI model tuning?
8. How do transformers handle long sequences?
9. What are attention heads in transformers?
10. How does a transformer encoder-decoder architecture work?
11. How do you handle context in long text generations?
12. How do you implement multi-modal GenAI pipelines?
13. How do you perform prompt engineering for complex tasks?
14. How do you evaluate generative models quantitatively?
15. What is the role of synthetic data in model generalization?
16. How do you implement model distillation?
17. How do you compress large GenAI models for deployment?
18. How do you handle bias in GenAI models?
19. How do you integrate GenAI models into applications?
20. How do you optimize inference for latency and memory?
21. How do you perform few-shot learning with LLMs?
22. How do you evaluate multimodal models?
23. What is chain-of-thought prompting?
24. How do you handle adversarial inputs in GenAI?
25. How do you implement domain-specific GenAI models?

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📓 Expert Level

1. How do you design scalable GenAI architectures?
2. How do you deploy GenAI models in production?
3. How do you perform distributed training for LLMs?
4. How do you monitor model drift in GenAI?
5. How do you implement online learning in GenAI systems?
6. How do you handle privacy and security in GenAI?
7. How do you implement AI alignment and safety measures?
8. How do you design multi-agent generative systems?
9. How do you perform evaluation at enterprise scale?
10. How do you optimize transformer models for edge devices?
11. How do you implement self-supervised pretraining?
12. How do you handle zero-shot and few-shot generation at scale?
13. How do you implement GenAI for real-time applications?
14. How do you integrate GenAI with existing cloud services?
15. How do you handle adversarial attacks in GenAI models?
16. How do you design multimodal AI agents?
17. How do you implement explainable GenAI (XAI) for complex tasks?
18. How do you optimize diffusion models for faster sampling?
19. How do you evaluate alignment of GenAI with human preferences?
20. How do you perform large-scale model evaluation benchmarks?
21. How do you implement GenAI pipelines in production MLOps?
22. How do you handle domain adaptation in GenAI models?
23. How do you implement iterative feedback loops with human evaluators?
24. How do you integrate GenAI for autonomous decision-making?
25. What are emerging trends and future directions in GenAI (AGI, autonomous agents, self-supervised learning)?

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