IVQ 101-150

Section 11: Multi-modal & Vision-Language GenAI (10 Questions)

  1. What is a multi-modal model and how does it differ from text-only LLMs?

  2. How does CLIP work and what’s its role in GenAI?

  3. What are common use cases for vision-language models like Flamingo or GPT-4V?

  4. How do you fine-tune a multi-modal model for a specific domain?

  5. What are embeddings in the context of images and text together?

  6. How would you build a caption generator for images using GenAI?

  7. What are the challenges in evaluating multi-modal models?

  8. What is VQGAN + CLIP and how does it generate art?

  9. Compare DALL·E vs. MidJourney vs. Stable Diffusion.

  10. How do you control image style or tone in GenAI outputs?

Section 12: Tooling, Frameworks, and Libraries (10 Questions)

  1. Compare Hugging Face Transformers vs. OpenLLM vs. LlamaIndex.

  2. What is the role of Guardrails AI in GenAI apps?

  3. How would you use LangGraph to manage agent state?

  4. What’s the difference between LangChain Agents and Tools?

  5. What does AutoGen enable beyond simple prompt chaining?

  6. How can you use DSPy for LLM optimization?

  7. What are Tool-Use models, and how are they trained?

  8. How do you use the Function calling feature in OpenAI?

  9. What are “planning” and “reflection” loops in GenAI agents?

  10. Compare orchestration using LangChain vs. Flowise vs. Haystack.

Section 13: Domain-Specific GenAI Use Cases (10 Questions)

  1. How can GenAI transform legal document analysis?

  2. What are the risks of using GenAI in healthcare applications?

  3. Describe how GenAI is used in financial document summarization.

  4. How do you use GenAI in customer support automation?

  5. How can GenAI help with resume screening and hiring?

  6. What are limitations of GenAI in high-stakes decision-making?

  7. How would you use GenAI for educational tutoring?

  8. How can you combine GenAI with IoT or edge devices?

  9. What role can GenAI play in software documentation?

  10. How is GenAI changing video or game content generation?

Section 14: Ethics, Safety, and Red Teaming (10 Questions)

  1. What are red teaming practices in GenAI model evaluation?

  2. How do you design safe prompts to reduce offensive outputs?

  3. What is prompt injection and how do you mitigate it?

  4. How do you ensure GDPR compliance in GenAI applications?

  5. How do you identify if a model output is manipulated or misleading?

  6. What are jailbreak prompts and how do LLMs get exploited?

  7. What is constitutional AI?

  8. How do you audit GenAI models for ethical compliance?

  9. What steps can be taken to prevent model misuse?

  10. What is traceability in GenAI outputs and why is it important?

Section 15: Business, ROI, and Product Impact (10 Questions)

  1. How do you estimate the ROI of adding GenAI to a product?

  2. How do you reduce token cost in GenAI API usage?

  3. What are the top metrics for success in a GenAI product launch?

  4. How can GenAI help speed up product iteration cycles?

  5. How would you pitch a GenAI solution to a non-technical stakeholder?

  6. How do you measure user satisfaction with GenAI features?

  7. What are the challenges in monetizing GenAI-powered features?

  8. How do you handle customer trust concerns with AI-generated content?

  9. What are examples of GenAI as a co-pilot in SaaS platforms?

  10. How would you build a GenAI roadmap for a startup?

PreviousIVQ 51-100NextIVQ 151-200

Last updated