Top 5 Generative AI (Gen AI) Interview Questions | Asked in Interviews 2024

Summary

In this video, Sait Patnayak discusses the growing shift from traditional AI to generative AI, highlighting its rising significance in the job market. The focus is on generative AI interview questions, including the differences between generative and traditional AI, key use cases, model evaluation, retrieval-augmented generation (RAG), and vector databases. The video also offers guidance on answering these questions for all experience levels, with an emphasis on providing industry-relevant examples. Additionally, Sait promotes a generative AI interview Q&A document available soon, offering valuable insights for anyone preparing for AI-related interviews.

Takeaways

• 😀 Generative AI is gaining momentum in the job market, with more companies investing in it compared to traditional AI and data science roles.
• 😀 The demand for generative AI roles is increasing, particularly in text generation, with future trends focusing on image and video generation.
• 😀 When answering generative AI interview questions, responses should vary based on experience—freshers should keep answers concise, while experienced candidates should provide detailed, industry-specific examples.
• 😀 The first key question in generative AI interviews is understanding how generative AI differs from traditional AI, with a focus on text generation and encoder-decoder architectures.
• 😀 Generative AI is centered around content generation, using models like Transformers (e.g., GPT), which is a major difference from traditional AI's predictive modeling and classification tasks.
• 😀 An important interview question is about specific generative AI use cases you’ve worked on; it’s essential to provide real-world examples to showcase your expertise.
• 😀 Evaluating generative AI models often requires human-level intervention, especially when using custom data or building chatbots, to assess the quality of outputs.
• 😀 RAGs (Retrieval-Augmented Generation) solve issues like hallucination and overfitting by incorporating external data retrieval before generating text, improving the quality and accuracy of responses.
• 😀 Vector databases are essential in generative AI for storing large amounts of vectorized data, like PDF documents converted into vectors for chatbots to use for fast retrieval.
• 😀 Vector databases differ from traditional databases by storing high-dimensional data and improving retrieval speed using techniques like locality-sensitive hashing.
• 😀 Understanding the differences between vector databases and vector indexes is crucial for implementing AI solutions; vector indexes are more suitable for prototypes, while vector databases are used for production-level systems.

Q & A

How has the AI industry changed in the past two years?

The AI industry has seen a significant shift from traditional machine learning and deep learning approaches to generative AI. Companies are increasingly investing in generative AI, which focuses on creating new data rather than just analyzing or predicting existing data. As a result, job opportunities in generative AI have risen, while traditional data science roles have decreased.

Why is there a growing demand for generative AI professionals?

Generative AI is rapidly gaining traction due to its ability to create new content, such as text, images, and videos. This has led to an increase in job opportunities for roles focused on generative AI, as companies look to leverage AI for tasks like content creation, summarization, and translation. The focus is on creating models that can generate useful and realistic outputs across various domains.

What should a fresher focus on when answering generative AI interview questions?

For a fresher, the answers should be concise and to the point, typically within 3-4 minutes for each question. It's important to focus on foundational knowledge, clear explanations, and simple examples. As you grow in experience, interviewers will expect more in-depth answers, often with real-world applications.

What is the main difference between traditional AI and generative AI?

Traditional AI focuses on predictive tasks such as recommendation systems, classification, and clustering, typically relying on historical data. In contrast, generative AI focuses on creating new data, like generating text, images, or other content. It uses advanced techniques such as transformer models, which include encoder-decoder architectures, to produce outputs based on prompts.

Can you describe a use case of generative AI that you've implemented?

A potential use case for generative AI could involve building a chatbot that answers questions based on a specific dataset, like research papers or medical records. The model would use techniques such as natural language processing (NLP) and summarization to provide accurate, context-aware responses to user queries.

How do you evaluate the performance of a generative AI model?

Evaluating generative AI models often requires human-level intervention to ensure the generated outputs are relevant and accurate. For example, when working with custom data, such as research papers or medical reports, human review is needed to verify the model's responses. In addition, domain expertise plays a crucial role in evaluating the relevance and correctness of the model's output.

What are Retrieval-Augmented Generation (RAG) models and how do they improve generative AI?

RAG models combine retrieval-based and generative methods. Retrieval focuses on fetching relevant data, augmentation enhances it, and generation produces the final output. This approach addresses issues like hallucination (where models generate incorrect or nonsensical information) and improves performance, especially when working with custom data sources, such as academic papers or medical reports.

What is the difference between a vector database and a traditional database?

A traditional database stores scalar data like integers or strings, whereas a vector database stores vectorized data, which is critical for generative AI tasks. Vector databases are designed to handle large datasets efficiently, allowing for fast retrieval of similar vectors. In contrast, traditional databases can suffer from high latency when dealing with large volumes of vectorized data.

Why are vector databases important for generative AI applications?

Vector databases are crucial for generative AI because they allow efficient storage and retrieval of vectorized data, which is used by models for tasks like text generation, image creation, and other generative functions. Storing vectors in a traditional database would lead to high latency, making it less suitable for generative AI tasks that require fast and accurate retrieval of information.

How do vector indexes differ from vector databases?

Vector indexes are local storage systems used to store vectorized data temporarily, often created on the fly during development or for proof-of-concept (POC) projects. In contrast, vector databases are more robust, physical databases designed for long-term storage and fast retrieval of vector data at scale. Vector indexes may work well for small-scale applications, but for large-scale generative AI systems, a dedicated vector database is preferred for its efficiency and scalability.