13 September 2025

#GenAI

#GenAI

Key Concepts


Topic SubTopics Basic Intermediate Advanced Expert
Introduction to GenAI Definition, Key Features, Use Cases, Differences from Traditional AI ✅
Generative Models GANs, VAEs, Diffusion Models, Autoregressive Models ✅ ✅ ✅
GANs (Generative Adversarial Networks) Generator, Discriminator, Training, Loss Functions ✅ ✅ ✅
VAEs (Variational Autoencoders) Encoder, Decoder, Latent Space, KL Divergence ✅ ✅ ✅
Diffusion Models Denoising, Score-based Models, Sampling Techniques ✅ ✅ ✅
Transformers in GenAI Attention Mechanism, Self-Attention, Encoder-Decoder ✅ ✅ ✅
Large Language Models (LLMs) GPT, BERT, LLaMA, Tokenization, Context Windows ✅ ✅ ✅
Prompt Engineering Zero-shot, Few-shot, Chain-of-thought, Prompt Tuning ✅ ✅ ✅
Fine-tuning & Adaptation Parameter-efficient Fine-tuning, LoRA, PEFT, Domain Adaptation ✅ ✅ ✅
Text Generation Autoregressive Text, Temperature, Top-k, Top-p Sampling ✅ ✅ ✅
Image Generation DALL-E, Stable Diffusion, ControlNet, Inpainting ✅ ✅ ✅
Audio & Music Generation Text-to-Speech, Music Synthesis, Voice Cloning ✅ ✅ ✅
Multimodal GenAI Text-to-Image, Text-to-Audio, Image-to-Text, Video Generation ✅ ✅ ✅
Evaluation Metrics Perplexity, FID, IS, BLEU, Human Evaluation ✅ ✅ ✅
Ethics & Safety Bias, Hallucinations, Deepfakes, Responsible AI, Mitigation ✅ ✅ ✅ ✅
Deployment & Scaling Model Serving, Cloud GenAI, API Integration, Latency Optimization ✅ ✅ ✅
Optimization & Compression Quantization, Pruning, Distillation, Efficient Inference ✅ ✅ ✅
Data & Training Pretraining Datasets, Data Augmentation, Synthetic Data ✅ ✅ ✅
Security & Privacy Differential Privacy, Federated Learning, Secure Data Handling ✅ ✅ ✅
GenAI in Industry Chatbots, Virtual Assistants, Content Creation, Gaming ✅ ✅ ✅ ✅
Research & Emerging Trends Foundation Models, Self-Supervised Learning, Multimodal LLMs ✅ ✅
Human-in-the-Loop (HITL) Feedback Loops, Reinforcement Learning with Human Feedback (RLHF) ✅ ✅ ✅
Evaluation & Benchmarking HELM, MMLU, HumanEval, AI Alignment Benchmarks ✅ ✅
Explainability & Interpretability Attribution Methods, Attention Visualization, Model Insights ✅ ✅ ✅
Future Directions AGI Pathways, AI Alignment, Autonomous GenAI Agents ✅ ✅

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?

📗 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?

📕 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?

📓 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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