Generative AI Interview Questions and Answers
Generative AI Interview Questions
Generative AI Interview Questions and Answers — Basic Level (1–10)
1) What is Generative AI?
Generative AI is a field of artificial intelligence focused on creating new content that resembles real data. By learning patterns and structures from existing datasets, it can generate fresh outputs such as text, images, audio, video, or even computer code.
2) What are the common real-world applications of Generative AI?
- Text generation (ChatGPT, copywriting tools)
- Image and video generation (MidJourney, DALL·E)
- Voice cloning and music creation
- Data augmentation for training ML models
- Drug discovery and molecule design
3) What is a GAN?
4) Explain the structure of a GAN.
Generator: Produces synthetic data from random noise.
Discriminator: Evaluates whether the data is real (from training) or fake (from the generator).
Both are trained in a minimax game until the generator produces realistic data.
5) What is a Variational Autoencoder (VAE)?
A VAE is a generative model that compresses data into a latent space and then reconstructs it back. Unlike standard autoencoders, VAEs generate new samples by sampling from the latent distribution.
6) How do GANs differ from VAEs?
- GANs use an adversarial approach (generator vs. discriminator).
- VAEs use probabilistic encoding/decoding.
- GANs often produce sharper images, while VAEs ensure better latent space continuity.
7) Can you explain what latent space means in generative models?
Latent space is the compressed representation of data. Each point in this space corresponds to a meaningful variation in the generated output (e.g., changing face shape or smile in image generation).
8) What challenges are commonly faced while training GANs?
- Mode collapse
- Training instability
- Sensitive hyperparameters (learning rate, batch size)
- Balancing generator vs. discriminator learning
9) What is mode collapse?
Mode collapse occurs when a GAN’s generator creates only a narrow set of outputs, repeating similar patterns instead of representing the complete variety present in the training data.
10) How can mode collapse be mitigated?
- Using Wasserstein loss (WGANs)
- Mini-batch discrimination
- Regularization techniques
- Adjusting training schedules
Generative AI Interview Questions and Answers — Basic Level (11–20)
11) Can you explain the function of the discriminator in a GAN?
The discriminator distinguishes between real and generated data, providing feedback to the generator to improve output quality.
12) What is overfitting in generative models?
Overfitting happens when a generative model memorizes training data instead of learning general patterns, producing poor generalization.
13) How can overfitting be avoided in generative AI models?
- Data augmentation
- Dropout layers
- Early stopping
- Regularization (L1/L2 penalties)
- Larger and more diverse datasets
14) Why is random noise used in GANs?
15) What is a conditional GAN (cGAN)?
16) How do cGANs differ from standard GANs?
- Standard GANs generate random samples.
- cGANs generate samples based on specific conditions (e.g., generating digits conditioned on label “5”).
17) What is an autoencoder?
18) What is the difference between an autoencoder and a VAE?
- Autoencoder: deterministic reconstruction.
- VAE: probabilistic reconstruction with latent distribution sampling.
19) What are deepfakes?
Deepfakes are synthetic videos or images created using generative AI, typically replacing one person’s face or voice with another’s.
20) What is the ethical concern surrounding deepfakes?
- Misinformation and fake news
- Privacy violations
- Identity theft and fraud
- Manipulation in politics or media
Generative AI Interview Questions and Answers — Basic & Intermediate (21–40)
21) Can you explain data augmentation and its importance in generative AI?
Data augmentation is the process of expanding a dataset by applying transformations like rotation, flipping, or adding noise. This technique helps prevent overfitting and enhances the model’s ability to generalize to new data.
22) What makes Generative AI different from traditional AI?
- Traditional AI: focuses on prediction, classification, decision-making.
- Generative AI: focuses on creating new data resembling training examples.
23) Why do we need generative AI?
- Fills gaps in limited datasets
- Enables creative applications (art, music, storytelling)
- Simulates real-world scenarios for testing
- Accelerates innovation in industries like healthcare and design
24) What is self-attention?
Self-attention is a mechanism where each element of input (e.g., a word in a sentence) relates to every other element, helping the model understand context and dependencies.
25) What is a language model?
A language model predicts the next word in a sequence based on context. Examples include GPT, BERT, and LLaMA.
26) How do autoregressive models work?
Autoregressive models generate outputs step by step, predicting each new element based on previous ones. Example: GPT generates text one word at a time.
27) What is OpenAI’s GPT?
Generative Pre-trained Transformer (GPT) is a powerful language model trained on massive text data, enabling it to generate human-like responses, answer queries, and perform tasks that require context understanding and reasoning.
28) What are the main building blocks of the Transformer architecture?
- Encoder & Decoder blocks
- Multi-head self-attention
- Feed-forward layers
- Layer normalization
- Positional encoding
29) What is a BERT model?
BERT (Bidirectional Encoder Representations from Transformers) is a transformer model trained using masked language modeling, enabling it to understand context in both directions.
30) How does GPT differ from BERT?
- GPT: autoregressive (predicts next token, mainly generative).
- BERT: bidirectional encoder (mainly for understanding tasks like classification, Q&A).
31) What is the role of a generator in a GAN?
The generator produces synthetic data from random noise with the goal of making it realistic enough to trick the discriminator into recognizing it as genuine.
32) What is pixel-wise loss in generative models?
Pixel-wise loss measures the difference between generated and target images at the pixel level (e.g., Mean Squared Error).
33) Why are GANs considered advantageous compared to other generative models?
GANs generate highly realistic data (images, videos, text) compared to older generative methods.
34) How does the discriminator improve its learning during GAN training?
The discriminator learns by classifying inputs as real or fake, adjusting its weights based on classification errors, and providing gradients to improve the generator.
35) What is the importance of the learning rate in GAN training?
A proper learning rate ensures stable training:
Too high → instability, poor convergence.
Too low → slow training, mode collapse risk.
36) What is the Wasserstein loss in GANs?
Wasserstein loss, used in WGANs, measures how far apart the real and generated data distributions are. Unlike standard GAN loss, it provides smoother gradients, leading to more stable and reliable training.
37) Why are Wasserstein GANs (WGANs) considered better than standard GANs?
WGANs address common GAN issues like mode collapse and unstable training by using Wasserstein distance instead of binary cross-entropy, resulting in better convergence and more reliable outputs.
38) Can you explain spectral normalization and its purpose in GANs?
Spectral normalization controls the weights of the discriminator to keep training stable. It prevents the discriminator from overpowering the generator, ensuring balanced and effective learning between both networks.
39) In what ways does the attention mechanism enhance generative models?
Attention helps models identify and focus on the most important parts of the input. In image generation, it captures fine details, while in text tasks, it preserves context and improves coherence.
40) What major challenges arise when training large-scale generative models?
- Huge computational cost
- Memory limitations
- Data availability and quality
- Training instability
- Ethical concerns like bias and misuse
Generative AI Interview Questions and Answers — Intermediate (41–50)
41) What is 'boosting' in ensemble learning?
Boosting is an ensemble technique where multiple weak learners are combined sequentially, with each model focusing on correcting the errors of the previous one.
42) How do the training approaches of GANs and VAEs differ?
- GANs: adversarial training (generator vs discriminator).
- VAEs: probabilistic reconstruction using KL divergence to regularize the latent space and reconstruction loss to match the input.
- GANs tend to produce sharper images, while VAEs emphasize a smooth, well-structured latent space.
43) Why is KL divergence used in VAEs?
KL divergence quantifies the difference between the model’s learned latent distribution and the prior distribution (commonly Gaussian). It keeps the latent space organized and continuous, enabling smoother generation.
44) What is a CycleGAN?
A CycleGAN is a GAN variant for unpaired image-to-image translation (e.g., horse ↔ zebra) that does not require paired datasets.
45) How do CycleGANs perform domain translation when paired data is not available?
They use cycle consistency loss: translating an image to another domain and then back should recover the original, enabling mapping without paired samples.
46) Why are skip connections used in generative models?
Skip connections (as in U-Net) pass features directly from encoder to decoder, preserving fine details and reducing information loss in deep networks.
47) Can I generate code using generative AI?
Yes. Models like OpenAI Codex and GitHub Copilot can generate code snippets, complete functions, and assist with debugging from natural-language prompts.
48) How does the discriminator get better during GAN training?
The discriminator iteratively updates its parameters by comparing real vs fake samples. As the generator improves, the discriminator adapts to spot subtler differences, maintaining a balanced competition.
49) Can you explain perceptual loss in the context of image generation?
Perceptual loss compares high-level features from a pre-trained network (e.g., VGG) rather than raw pixels, yielding outputs that look more visually realistic to humans.
50) How does a generator differ from a decoder?
A generator creates synthetic data from noise or conditions (GANs), while a decoder reconstructs original input from a compressed latent representation (autoencoders/VAEs). The key difference: generators create new data; decoders rebuild existing data.