Important Generative AI Interview Questions

If you’re learning LLM or Generative AI and planning to attend an interview, these Generative AI interview questions will be very helpful. After doing lots of research, I found some important questions that will assist you in cracking your Gen AI interview.

1. What are Large Language Models (LLMs) and how do they work?
2. Describe the architecture of a transformer model that is commonly used in LLMs.
3. What are the main differences between LLMs and traditional statistical language models?
4. How is GPT-3 different from its predecessors like GPT-2 in terms of capabilities and applications?
5. Can you mention any domain-specific adaptations of LLMs?
6. How do LLMs contribute to the field of sentiment analysis?
7. Describe how LLMs can be used in the generation of synthetic text.
8. In what ways can LLMs be utilized for language translation?
9. Discuss the application of LLMs in conversation AI and chatbots.
10. Explain how LLMs can improve information retrieval and document summarization.
11. Describe the BERT (Bidirectional Encoder Representations from Transformers) model and its significance.
12. Can you explain the concept of attention mechanisms in transformer models?
13. What are positional encodings in the context of LLMs?
14. Discuss the significance of pre-training and fine-tuning in the context of LLMs.
15. How do LLMs handle context and long-term dependencies in text?
16. What is the role of transformers in achieving parallelization in LLMs?
17. What are some prominent applications of LLMs today?
18. Explain the core idea behind the T5 (Text-to-Text Transfer Transformer) model.
19. What is the RoBERTa model and how does it differ from standard BERT?
20. Discuss the technique of ‘masking’ in transformer models like BERT.
21. How does the GPT (Generative Pre-trained Transformer) series of models work?
22. What are some of the limitations of the Transformer architecture in LLMs?
23. How do hyperparameters affect the performance of LLMs?
24. Discuss the role of learning rate schedules in training LLMs.
25. What is the importance of batch size and sequence length in LLM training?
26. Explain the concept of gradient checkpointing in the context of training efficiency.
27. How can one use knowledge distillation in the context of LLMs?
28. Discuss techniques for reducing the memory footprint of LLMs during training.
29. What preprocessing steps are crucial when dealing with input data for LLMs?
30. How is tokenization performed in the context of LLMs, and why is it important?
31. Discuss the process of vocabulary creation and management in LLMs.
32. What considerations should be taken into account for handling different languages in LLMs?
33. How do you address the challenge of overfitting in LLMs?
34. Discuss strategies for efficient deployment of LLMs in production environments.
35. Can you describe techniques to monitor and maintain LLMs in production?
36. Explain the factors to consider when selecting hardware for training LLMs.
37. Discuss the role of multi-GPU and distributed training in LLMs.
38. Write a Python function using PyTorch or TensorFlow to tokenize input text for GPT-2.
39. Implement a simple transformer block using PyTorch or TensorFlow.
40. How do you evaluate the performance of LLMs?
41. Discuss the challenges of evaluating LLMs in a real-world context.
42. How can LLMs be fine-tuned for specific tasks?
43. Explain the concept of transfer learning in the context of LLMs.
44. What is the role of embeddings in LLMs?
45. Discuss how LLMs handle out-of-vocabulary (OOV) words.
46. How do LLMs address the issue of bias in generated text?
47. What are some common pitfalls in training LLMs?
48. Explain the importance of ethical considerations in the deployment of LLMs.
49. How do you handle the privacy concerns associated with LLMs?
50. Describe the significance of model interpretability in LLMs.
51. What are Zero-shot and few-shot learning capabilities in LLM?
52. Discuss the implications of attention flow in multi-head attention mechanisms.
53. What are the potential future applications of LLMs that are currently being researched?
54. How can reinforcement learning be applied to further training or fine-tuned LLMs?
55. Discus Generative Adversarial networks ( GAN ) in the context of text generation with LLMs.
56. Describe a method for efficiently rollback to the previous LLM model state in case of failures.
57. Explain model versioning strategies when updating LLMs in production. 
58. How would you conduct A/B testing for a new version of an LLM-based application?
59. What metrics would you use to evaluate the performance of a fine-tuned LLM?
60. Propose a framework to use LLMs in creating personalized content recommendations.
61. How would you set up a LLM to create a news article summarizer?
62. What approach would you take to build a chatbot using LLM?
63. Design a system using LLMs to generate code snippets from natural language descriptions.
64. Discus techniques to adapt a LLM for a legal document review application.
65. Modify a pre-trained BERT model for a classification task using transfer learning.
66. Implement a beam search algorithm for better text generation models.
67. Develop a custom loss function for a transformer model that accounts for both forward and backward prediction.
68. Fine-tuned a GPT-2 model for a specific task style or author using PyTorch or tensorflow.
69. Code a routine to perform abstractive text summarization using a pre-trained T5 model.
70. Hallucination in LLMs is a known issue, how can you evaluate and mitigate it?
71. What evaluation metrics can be used to judge LLM generation quality?
72. When generating sequences with LLMs, how can you handle long context lengths efficiently? Discuss techniques for managing long inputs during real-time inference.
73. Large Language Models often require careful tuning of learning rates. How do you adapt learning rates during training to ensure stable convergence and efficient learning for LLMs?
74. Explain the concept of triplet loss in the context of embedding learning.
75. What is quantization in the context of embeddings, and how does it contribute to reducing the memory footprint of models while preserving representation quality?

I hope this blog helps you to crack your upcoming GEN AI Interview. Feel free to ask your valuable questions in the comments section below.