12. How would you build a customer support AI agent?
Answer:
Components: Knowledge Base, RAG, LLM, Ticketing Integration, CRM Integration, Monitoring
Capabilities:** Answer FAQs, Create tickets, Escalate issues, Summarize conversations
13. Users complain that responses are too slow. How would you improve latency?
Answer:
Smaller models, Response caching, Faster vector search, Prompt optimization, Streaming responses, Infrastructure scaling
14. What would you monitor in a production GenAI application?
Answer:
Monitor: Latency, Token usage, Costs, Error rates, Hallucinations, User feedback, Retrieval quality
15. How would you handle sensitive company data in an LLM application?
Answer:
Access controls, Encryption, Data masking, Private deployments, Audit logging, Secure APIs
Security is critical for enterprise AI systems.
16. How would you design a GenAI-powered resume screening solution?
Answer:
Workflow:
1. Upload resumes
2. Extract text
3. Compare with job description
4. Calculate match score
5. Generate summary
6. Rank candidates
17. What would you do if retrieved context exceeds the context window?
Answer:
Solutions: Better chunking, Summarization, Reranking, Context compression, Top-k optimization
Only send the most relevant information to the model.
18. How would you build a multi-document RAG system?
Answer:
Architecture: Multiple data sources, Unified embedding pipeline, Vector database, Metadata filtering, Reranking layer, LLM response generation
19. What are the biggest challenges when deploying GenAI applications?
Answer:
Hallucinations, Cost management, Security, Latency, Scaling, Monitoring, Compliance, Data privacy
20. Design an enterprise GenAI architecture for a bank.
Answer:
Architecture:
Users
↓
Web Application
↓
API Gateway
↓
Authentication
↓
RAG Layer
↓
Vector Database
↓
LLM
↓
Monitoring and Logging
Additional components: Data Encryption, Access Control, Audit Logs, Guardrails, Human Approval Layer
This design ensures scalability, security, compliance, and reliability.
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