Building AI Applications: Interview Questions for Production Systems

From Prototype to Production

Building AI applications that work in demos is easy. Building ones that work in production is hard. Here's what interviewers want to know about your production AI experience.

Architecture Questions

Q: Describe the architecture of an AI application you've built.

Structure your answer:

1. Problem statement and requirements
2. High-level architecture diagram
3. Key components and their responsibilities
4. Data flow through the system
5. Trade-offs you made and why

Q: How do you handle the non-determinism of LLMs in production?

Strong Answer: "Non-determinism is a core challenge. Strategies I use:

• Structured outputs (JSON mode, function calling) for parsing reliability
• Output validation and retry logic
• Temperature 0 for consistency when appropriate
• Caching identical requests
• Guardrails for output quality
• Graceful degradation when confidence is low
• Comprehensive logging for debugging"

Q: How do you design for LLM provider failures?

Key strategies:

• Multiple provider fallbacks (OpenAI → Anthropic → self-hosted)
• Circuit breakers to prevent cascade failures
• Request queuing with retries
• Graceful degradation (cached responses, simpler models)
• Health monitoring and alerting
• SLAs with realistic expectations

Cost Optimization

Q: How do you manage LLM costs in production?

Strong Answer: "Cost management is critical as usage scales. My approach:

• **Tiered models**: Route simple queries to cheaper/smaller models
• **Caching**: Cache common queries, embedding results
• **Prompt optimization**: Shorter prompts where possible
• **Batch processing**: When latency allows, batch requests
• **Context management**: Only include necessary context
• **Usage monitoring**: Track costs per feature/user
• **Rate limiting**: Prevent runaway costs from abuse"

Q: When would you fine-tune vs use prompting vs use RAG?

Decision framework:

• **Prompting**: First approach, sufficient for many tasks, most flexible
• **RAG**: When you need current/private information, factual grounding
• **Fine-tuning**: When you need specific style/format, domain expertise, or to reduce prompt length

Fine-tuning is expensive and inflexible. Start with prompting + RAG.

Evaluation and Testing

Q: How do you test AI applications?

Testing pyramid:

• **Unit tests**: Individual components (parsers, validators)
• **Integration tests**: API calls, database interactions
• **Prompt tests**: Fixed inputs → expected outputs (regression)
• **Evaluation sets**: Diverse test cases with human-judged answers
• **Shadow testing**: Run new versions alongside production
• **A/B testing**: Statistical comparison with real users

Q: How do you handle prompt regression?

Strong Answer: "Prompt changes can have unexpected effects. My process:

• Maintain evaluation datasets (100+ examples per major feature)
• Automated testing on every prompt change
• Track metrics across versions
• Staged rollouts with monitoring
• Easy rollback capability
• Document why each prompt change was made"

Monitoring and Observability

Q: What do you monitor in AI applications?

Essential metrics:

• **Latency**: P50, P95, P99 response times
• **Error rates**: API failures, parsing failures, validation failures
• **Quality metrics**: User feedback, task completion rates
• **Cost**: Per request, per user, per feature
• **Token usage**: Input/output tokens, context utilization
• **Model behavior**: Refusals, hallucination indicators

Q: How do you debug AI application issues?

Strong Answer: "Debugging non-deterministic systems is challenging. I ensure:

• Comprehensive request/response logging
• Trace IDs across the full pipeline
• Prompt versioning and logging
• Reproducibility tooling (save full context for replays)
• Error categorization and alerting
• User feedback collection
• Regular sample review of outputs"

Security and Safety

Q: What security concerns are unique to AI applications?

Key concerns:

• **Prompt injection**: User input manipulating model behavior
• **Data leakage**: Model exposing training or context data
• **PII handling**: What gets sent to APIs, logged, stored
• **Output safety**: Harmful, biased, or inappropriate responses
• **API key management**: Secure storage and rotation
• **Rate limiting**: Preventing abuse

Q: How do you implement content moderation?

Strategies:

• Input filtering before LLM
• Output filtering after LLM
• Model-based classification (toxicity, PII)
• Human review queues for edge cases
• User reporting mechanisms
• Blocklists and allowlists

Scaling Considerations

Q: How do you scale AI applications?

Key scaling strategies:

• **Horizontal scaling**: Stateless services, load balancing
• **Async processing**: Queue long-running tasks
• **Caching**: At multiple layers (embeddings, responses)
• **CDN**: For static assets and cached responses
• **Database optimization**: Connection pooling, read replicas
• **Provider limits**: Multiple API keys, provider fallbacks

Q: How do you handle traffic spikes?

Strong Answer: "AI applications are particularly sensitive to spikes because of API rate limits and costs. Strategies:

• Request queuing with priority
• Rate limiting per user/tier
• Auto-scaling with appropriate limits
• Circuit breakers to shed load gracefully
• Degraded mode with cached/simpler responses
• Pre-provisioned capacity for known events"

System Design Example

"Design a customer support chatbot for an e-commerce company."

Key components:

1. Chat interface (web/mobile)
2. Intent classification
3. Knowledge base (RAG) for product/policy info
4. Order system integration
5. Escalation to human agents
6. Analytics and feedback loop

Design considerations:

• Conversation history management
• Context from user's account/orders
• When to escalate vs self-serve
• Multi-language support
• Response latency targets
• Cost per conversation

Real-World Experience

Prepare stories about:

• A production AI bug and how you debugged it
• Cost optimization you implemented
• Quality improvement you measured
• Scaling challenge you overcame
• Safety/security issue you addressed

Production AI experience is increasingly valuable. Focus on the operational aspects that distinguish production systems from prototypes.