Why This Is Asked
Conversational AI support is one of the most common real-world LLM applications. This question tests your ability to combine multiple AI techniques into a coherent system with real product constraints.
Key Concepts to Cover
- Intent classification — routing queries to the right handler
- RAG for knowledge retrieval — grounding answers in company documentation
- Multi-turn context — maintaining conversation state across messages
- Human escalation — when and how to hand off to a human agent
- Guardrails — preventing the bot from making wrong commitments
- Feedback collection — measuring resolution rate and CSAT
How to Approach This
1. Clarify Requirements
- What's the domain? (e-commerce, SaaS, banking)
- What percentage of queries should resolve automatically?
- What's the escalation path?
- What data sources are available?
- Any compliance requirements?
2. High-Level Architecture
User Message → Intent Classifier → Router
├── FAQ/Info queries → RAG Pipeline → LLM Response
├── Account queries → API Integration → LLM Response
├── Complex/emotional → Human Agent Queue
└── Out-of-scope → Deflection + Escalation
3. Multi-Turn Context
- Store conversation turns in Redis with TTL
- Include recent N turns in every LLM prompt
- Summarize old turns to fit context limits
- Preserve key entities (order numbers, account IDs) across turns
4. Guardrails
- Never let the bot commit to refunds, timelines, or policies it can't verify
- Use structured output to separate "facts" from "suggestions"
- Log all bot responses for audit
5. Measuring Success
- Containment rate: % resolved without human escalation
- CSAT: customer satisfaction score
- False resolution rate: cases where bot said "resolved" but customer came back
Common Follow-ups
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"How would you handle an angry or distressed customer?" Sentiment detection to trigger immediate human escalation, empathetic response templates.
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"How do you keep the bot's knowledge up to date?" Automated re-indexing on documentation updates, version-controlled prompt updates, A/B testing new knowledge base versions.
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"What happens when the bot gives a wrong answer?" Logging all responses, a "report a problem" button, post-conversation review for flagged cases.