IVQ 801-850

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Section 81: Tool Use, Accuracy & Function Calling (10 Questions)

1. How do you evaluate if an LLM chose the correct tool for a task?

2. What are common failure modes when chaining tools and LLM outputs?

3. How do you validate arguments passed to external functions by an LLM?

4. What’s the difference between tool calling and API orchestration?

5. How do you prompt an LLM to ask for help instead of hallucinating?

6. How do you implement retries or fallbacks for tool failures mid-generation?

7. How do you prevent recursive tool use in chain-of-thought agents?

8. What’s the best way to log tool usage alongside LLM token data?

9. How would you test multiple tool-choice agents for accuracy and safety?

10. How can a tool-using LLM gracefully degrade to a “no tools” fallback?

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Section 82: PromptOps, Marketplaces & Prompt Engineering at Scale (10 Questions)

1. What is PromptOps and why is it needed in large orgs?

2. How do you manage prompt versioning across teams and environments?

3. What tools exist for prompt linting and testing?

4. How would you design a prompt approval or review workflow?

5. How do prompt marketplaces differ from model marketplaces?

6. How do you track prompt performance across different LLMs?

7. How do you guard against prompt duplication in a multi-team org?

8. What are the pros and cons of using shared prompt libraries in enterprise settings?

9. How do you track prompt drift when teams manually tune prompts over time?

10. How do you govern prompt security when prompts encode sensitive logic or PII?

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Section 83: Vector Search Optimization & Evaluation (10 Questions)

1. How do you tune chunking parameters for best RAG performance?

2. What are the trade-offs between cosine similarity and dot product in vector search?

3. How would you evaluate the quality of a vector index over time?

4. What is index rebalancing, and when should you perform it?

5. How does embedding dimensionality affect retrieval latency and accuracy?

6. How do you handle semantic overlap or redundancy in large corpora?

7. What are good practices for hybrid search (vector + keyword)?

8. How would you do a/b tests between Qdrant, Weaviate, and FAISS?

9. What metrics help identify poor grounding due to retrieval errors?

10. How do you compress or quantize vector indexes without hurting search performance?

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Section 84: No-Code / Low-Code GenAI Builders (10 Questions)

1. How would you evaluate low-code GenAI tools like Flowise or Buildship?

2. What’s the benefit of no-code LLM agents for prototyping workflows?

3. How do you expose prompt logic safely to business users?

4. How do you track logic or prompt branching in visual LLM builders?

5. How do you add testing and validation layers on top of low-code pipelines?

6. What are the common security risks with drag-and-drop GenAI workflows?

7. How do you support data privacy in no-code RAG apps?

8. How do you connect a low-code agent to external APIs securely?

9. What are the best ways to reuse components across GenAI canvas tools?

10. How would you teach product managers to use no-code GenAI tools effectively?

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Section 85: Personalized LLM Workflows (10 Questions)

1. How do you create persistent memory in user-specific GenAI sessions?

2. What’s the best way to store and retrieve user preferences for response generation?

3. How would you design a memory injection system for user context?

4. What are ethical limits around long-term LLM memory for user profiling?

5. How do you personalize tone, format, or content structure per user?

6. How can you use embeddings to cluster users with similar interaction styles?

7. What are cost-effective architectures for one-model-multi-persona support?

8. How do you segment prompts or logic based on user role or intent?

9. How would you implement feedback-driven personalization in a chat UI?

10. How do you build trust when GenAI adapts to users over time?

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