Basic Gen AI Interview Questions
What is Generative AI?
Generative AI refers to AI systems that can create new content (text, images, audio, code) based on patterns learned from existing data. Example: ChatGPT generating text.How is Generative AI different from traditional AI?
Traditional AI focuses on prediction and classification (e.g., spam detection), while Generative AI creates new data (e.g., writing an article or generating an image).What are the key components of Generative AI models?
- Neural Networks (foundation)
- Training Data
- Loss Function
- Optimization Algorithm
- Architecture (e.g., Transformer, GAN)
Explain neural networks in simple terms.
A neural network is like a web of connected nodes (neurons) that process data in layers, learning patterns by adjusting weights through training.What is a Transformer model?
A Transformer is an architecture that uses attention mechanisms to process sequences (like text) efficiently, enabling models like GPT and BERT.Difference between Machine Learning and Generative AI?
ML learns patterns for prediction, while Generative AI uses those patterns to generate new content.Real-world applications of Generative AI?
Chatbots, image generation (DALL·E), music composition, code generation, drug discovery.Define NLP.
Natural Language Processing is the field of AI that deals with understanding and generating human language.What is Deep Learning, and how does it relate to AI?
Deep Learning is a subset of AI using multi-layer neural networks for complex tasks like vision and language.Main advantages of Generative AI models?
- Creativity
- Automation
- Personalization
- Scalability
Core Technical Questions
What are GANs?
Generative Adversarial Networks are models with two components (Generator & Discriminator) that compete to create realistic data.Explain how GANs work.
Generator creates fake data → Discriminator checks real vs fake → Both improve through feedback.Role of Generator and Discriminator?
- Generator: Creates synthetic data.
- Discriminator: Judges authenticity.
What is a Diffusion Model?
A model that gradually removes noise from data to generate high-quality images (used in Stable Diffusion).Explain VAEs.
Variational Autoencoders compress data into a latent space and then reconstruct it, useful for generating variations.How are Transformers used in Generative AI?
They power LLMs by handling long text sequences efficiently using attention.What is an LLM?
Large Language Model trained on massive text data to understand and generate language (e.g., GPT-4).Examples of LLMs?
GPT, BERT, LLaMA, PaLM.How does attention mechanism work?
It assigns weights to words based on relevance, improving context understanding.What is tokenization in NLP?
Breaking text into tokens (words, subwords) for processing.
AI Interview Questions for Freshers
Common programming languages in AI?
Python, R, Java, C++, Julia.What is supervised learning?
Learning from labeled data (input-output pairs).Unsupervised learning?
Finding patterns in unlabeled data (e.g., clustering).Reinforcement learning?
Learning through trial and error using rewards.Example of supervised learning in daily life?
Email spam detection.What is overfitting?
Model performs well on training data but poorly on new data. Avoid using regularization, dropout, more data.Underfitting?
Model too simple → fails to learn patterns.Cross-validation?
Splitting data into folds to validate model performance.Regularization?
Technique to reduce overfitting by penalizing large weights.Hyperparameters?
Settings like learning rate, batch size that control training.
Artificial Intelligence Basic Interview Questions
Difference between AI and data science?
AI builds intelligent systems, data science focuses on analyzing data.AI impact on business decisions?
Predict trends, automate tasks, personalize services.Ethical challenges in AI?
Bias, privacy, transparency.Bias in AI and reduction?
Bias = unfair predictions. Reduce via diverse data, fairness checks.Model interpretability?
Ability to explain predictions.Turing Test?
Test to check if AI can mimic human intelligence.NLG?
Natural Language Generation = AI writing text.How does ChatGPT work?
Uses Transformer-based LLM trained on large text datasets.Embeddings in NLP?
Numeric representation of words for models.Fine-tuning?
Adapting a pre-trained model to a specific task.
Technical Focus
Gradient descent?
Optimization method to minimize loss by adjusting weights.Activation functions?
Introduce non-linearity (e.g., ReLU, Sigmoid).Loss functions?
Measure prediction error (e.g., Cross-Entropy).Backpropagation?
Algorithm to update weights using error gradients.CNN vs RNN?
CNN = images, RNN = sequences.Pre-trained model?
Model trained on large data, reused for tasks.Transfer learning benefits?
Saves time, improves accuracy.Limitations of AI models?
Bias, data dependency, lack of interpretability.Data augmentation?
Creating synthetic variations of data.Prompt engineering?
Crafting effective prompts for LLMs.
Domain & Application
Generative AI in healthcare?
Drug discovery, medical imaging.AI in marketing?
Personalized ads, content generation.AI in finance?
Fraud detection, risk analysis.AI in education?
Personalized learning, grading automation.AI in data analytics?
Predictive insights, anomaly detection.Challenges in deployment?
Scalability, bias, cost.AI in business automation?
Automates repetitive tasks.Future of Gen AI in India?
Huge growth in education, healthcare, IT.AI in mobile apps?
Voice assistants, personalization.Industries most affected?
IT, healthcare, finance, media.
Career & Training
Skills for AI engineer?
Python, ML, DL, data handling, cloud.Is data science a good career in India?
Yes, high demand and pay.Difference between data analytics & data science?
Analytics = insights, Science = predictive models.Why Python for AI?
Libraries, simplicity.Start learning Gen AI?
Basics of ML → DL → Transformers → Hands-on projects.
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