Generative AI interview questions
1. What is Generative AI?
Generative AI is a type of artificial intelligence that can create new content like text, images, music, or code. It learns from existing data and then generates something new. For example, ChatGPT creates human-like text responses based on training data.
2. How is Generative AI different from Traditional AI?
Traditional AI mostly classifies or predicts based on given data. Generative AI goes a step further — it creates new data that didn’t exist before.
3. What are some examples of Generative AI tools?
ChatGPT, DALL·E, Midjourney, Stable Diffusion, and GitHub Copilot are popular examples. They generate text, images, and even code.
4. What is a Large Language Model (LLM)?
An LLM is an AI model trained on massive datasets of text to understand and generate human-like language. GPT-4 is an example of an LLM.
5. What are the main components of a generative model?
They usually include an encoder (to understand input), a decoder (to generate output), and a training dataset.
6. What are transformers in AI?
Transformers are a type of neural network architecture that uses attention mechanisms to process sequences, like sentences, more efficiently. They’re behind GPT and BERT models.
7. Explain the concept of attention mechanism?
Attention lets a model focus on the most relevant parts of the input when generating output, improving accuracy and context understanding.
8. What is the difference between GPT and BERT?
GPT is a generative model that predicts the next word in a sequence, while BERT is designed for understanding text by looking at words in both directions (bidirectional).
9. What are embeddings in NLP?
Embeddings are numeric representations of words or sentences that capture their meaning. Models use embeddings to understand relationships between words.
10. What is prompt engineering?
Prompt engineering is crafting effective inputs (prompts) to guide an AI model’s output. For example, adding context or format instructions improves results.
11. How is Generative AI used in e-commerce?
It creates product descriptions, generates marketing copy, personalizes recommendations, and powers chatbots for customer support.
12. How can Generative AI help in healthcare?
It can generate synthetic medical images for training, create patient report drafts, and assist in drug discovery.
13. Give an example of Generative AI in education?
AI can generate personalized learning materials, quizzes, and explanations based on a student’s level.
14. How is Generative AI used in finance?
It can draft reports, summarize market trends, and generate synthetic data for fraud detection training.
15. How can startups in Hyderabad use Generative AI?
They can use it to automate content creation, prototype ideas faster, and build AI-powered products without huge teams.
16. What is fine-tuning in Generative AI?
Fine-tuning is training an existing model on a smaller, domain-specific dataset to make it perform better for a specific task.
17. What is zero-shot learning?
It’s when a model performs a task without having been explicitly trained on examples of that task.
18. What is few-shot learning?
The model is given a few examples during the prompt to help it perform a new task more accurately.
19. Why is large-scale data important for Generative AI?
The more diverse and high-quality the data, the better the AI can learn patterns and generate realistic outputs.
20. What is transfer learning in AI?
It’s when knowledge from one model or task is reused in another, saving time and resources in training.
21. What are the main ethical concerns addressed in Generative AI interview questions?
Many Generative AI interview questions focus on ethics because AI systems can impact society in powerful ways. Key concerns include the risk of bias in model outputs, which can lead to unfair or discriminatory results; misinformation, where AI generates false or misleading content; intellectual property issues, such as AI using copyrighted material without permission; and the creation of deepfakes, which can be used for fraud or manipulation. Interviewers want to see that you not only understand these risks but also know how to mitigate them — for example, by using diverse training data, applying content filters, adding human review processes, and following AI ethics guidelines.
22. What is AI hallucination?
When an AI generates content that sounds correct but is factually wrong or made-up.
23. How can bias in AI models be reduced?
By using diverse training data, auditing models regularly, and applying fairness algorithms.
24. What are deepfakes?
AI-generated videos or audio that mimic real people, often used for misinformation or fraud.
25. Why is explainability important in AI?
It helps users understand why an AI made a certain decision, building trust and enabling debugging.
26. What is LangChain?
LangChain is a framework for building applications using LLMs, enabling chaining of prompts and data sources.
27. What is Stable Diffusion?
It’s an open-source AI model for generating images from text prompts.
28. What is OpenAI’s GPT?
GPT is a series of language models by OpenAI designed to generate human-like text.
29. What is Midjourney?
It’s an AI tool for creating images based on text descriptions.
30. What is RLHF (Reinforcement Learning with Human Feedback)?
A training method where AI models are fine-tuned based on human preferences to improve output quality.
31. How do you integrate Generative AI into a web application?
You can use APIs like OpenAI’s GPT API or Hugging Face’s models and call them from your application backend.
32. How do you control the creativity of AI output?
By adjusting parameters like temperature (higher = more creative, lower = more predictable).
33. What is tokenization in NLP?
Breaking text into smaller units (tokens) such as words or subwords for processing by AI models.
34. What are common evaluation metrics for text generation?
BLEU, ROUGE, METEOR, and human evaluation.
35. What is overfitting in AI models?
When a model performs well on training data but poorly on unseen data because it learned patterns too specifically.
36. Which programming languages are most useful for Generative AI?
Python (most common), along with libraries like TensorFlow, PyTorch, and Hugging Face.
37. Do you need a PhD to work in Generative AI?
No. Many AI engineers are self-taught or have taken online courses, but strong fundamentals in math and programming help.
38. How do you showcase Generative AI skills in an interview?
By showing projects on GitHub, explaining your process, and demonstrating a working AI app or notebook.
39. What is the role of cloud platforms in AI?
AWS, Azure, and Google Cloud provide infrastructure and prebuilt AI services to build and deploy models.
40. How can a fresher get into Generative AI roles?
By learning Python, ML basics, LLM tools, building projects, and applying for AI internships.
41. What is multimodal AI?
AI that processes and generates multiple types of data (e.g., text, images, audio) together.
42. What are autonomous agents in AI?
AI systems that can plan and take actions on their own to achieve goals, like Auto-GPT.
43. How will Generative AI impact software development?
It will automate parts of coding, testing, and documentation, making developers more productive.
44. What is the role of Generative AI in the Metaverse?
It can create virtual environments, characters, and interactive experiences dynamically.
45. Will Generative AI replace jobs?
It will change job roles by automating repetitive tasks but also create new AI-related careers.
46. Tell me about a Generative AI project you worked on.
Explain the problem, tools used, your role, and the results. Highlight measurable impact.
47. How do you keep your AI skills updated?
Mention reading research papers, following AI blogs, joining communities, and experimenting with new tools.
48. Describe a challenge you faced in building an AI model?
Talk about technical hurdles, how you solved them, and what you learned.
49. How do you ensure AI outputs are ethical?
By testing for bias, validating data sources, and using human review when necessary.
50. Why should we hire you for an AI role?
I bring strong technical skills in Python, machine learning, and deep learning frameworks like TensorFlow and PyTorch. I have hands-on project experience building AI-powered chatbots, text generators, and image synthesis tools. My problem-solving ability allows me to break down complex challenges into simple, actionable steps, and my passion for AI keeps me constantly learning new trends and tools. I’ve also prepared extensively for Generative AI interview questions, so I can demonstrate both theoretical knowledge and practical application in real-world scenarios — making me ready to contribute from day one.
