IVQ 901-950

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Section 91: Evaluating LLM Agents & Reasoning Chains (10 Questions)

1. How do you measure the accuracy of a multi-step reasoning agent?

2. What are good benchmarks for evaluating chain-of-thought quality?

3. How do you test the consistency of agent outputs across repeated tasks?

4. What metrics track agent helpfulness beyond task completion?

5. How would you identify when agents hallucinate tools or intermediate steps?

6. What is “rationality scaffolding” and how does it affect agent performance?

7. How do you evaluate how well an agent follows task decomposition prompts?

8. What logging structure do you use for replaying agent reasoning steps?

9. How would you A/B test two agent policies for summarization tasks?

10. How do you evaluate emergent behavior in autonomous GenAI agents?

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Section 92: Multi-Turn Dialogue Flow & Control (10 Questions)

1. How do you preserve state across long multi-turn GenAI chats?

2. How do you detect when a conversation has changed topics?

3. How do you guide a user toward clarification if their intent is unclear?

4. What are patterns for handling ambiguous follow-up questions?

5. How do you reset, pause, or bookmark conversation state in chat UX?

6. How do you control verbosity across turns in dialogue generation?

7. How do you build dialogue guards against prompt hijacking?

8. What is response chaining, and how does it help maintain coherence?

9. How would you test a multi-turn GenAI assistant for regression errors?

10. How do you tune LLM temperature and top-p settings dynamically across chat flow?

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Section 93: GenAI-Powered Search Systems (10 Questions)

1. How do you combine dense and sparse retrieval in a GenAI search UI?

2. How would you design a QA system using search → rerank → generate?

3. How do you measure RAG latency, grounding score, and search recall together?

4. How do you detect and handle irrelevant passages retrieved by vector DBs?

5. How do you apply intent detection to improve query reformulation?

6. How do you store user feedback to fine-tune search responses over time?

7. What is cross-encoder reranking, and when should you use it in RAG?

8. How do you perform semantic deduplication on retrieved documents?

9. What’s your architecture for search + chat hybrid assistants?

10. How do you test search QA systems for hallucinated citations?

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Section 94: Localization, Translation & Cultural Context (10 Questions)

1. How do you evaluate LLM translation output across low-resource languages?

2. What role do locale embeddings play in multilingual retrieval systems?

3. How do you ensure tone consistency across translations in GenAI?

4. What are techniques for preserving names, measurements, and idioms during translation?

5. How would you fine-tune a model on bilingual customer support transcripts?

6. How do you detect cultural insensitivity in GenAI outputs?

7. How do you create prompt templates for region-specific language variants?

8. What’s the difference between transliteration, translation, and localization in GenAI flows?

9. How do you handle code-mixing in multilingual chat systems?

10. How do you evaluate GenAI-generated content for culturally appropriate phrasing?

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Section 95: Resilience, Fallbacks & Fail-Safe Strategies (10 Questions)

1. What are good strategies for handling failed LLM completions gracefully?

2. How do you design multi-step workflows with retry logic per stage?

3. How would you implement a fallback to search-based responses?

4. How do you monitor LLM token quota exhaustion in high-traffic systems?

5. What caching strategies help improve availability of previous outputs?

6. How do you gracefully degrade to static answers when LLMs are offline?

7. How do you validate whether an output should be retried or accepted?

8. What’s your plan for cross-provider redundancy in GenAI infrastructure?

9. How do you implement guardrails against silent failures in agent workflows?

10. What’s the difference between circuit breakers, retries, and human escalation in GenAI pipelines?

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