LLMOps 与生产应用
How does Prompt Caching work?
原题Prefill vs Decode
原题Why is prefill compute-bound and decode memory-bandwidth-bound?
原题What is chunked prefill, and why does it improve tail latency under mixed traffic?
原题What is Prefill-Decode Disaggregation, and when does it pay off?
原题Explain the AI product lifecycle from ideation to production.
原题What is LLMOps, and how does it differ from traditional MLOps?
原题How do you serve LLMs in production?
原题What is model quantization?
原题Explain post-training quantization (PTQ) vs quantization-aware training (QAT). What breaks when you push weights to 2-4 bits?
原题How do you monitor LLM applications in production?
原题What is LLM observability?
原题What are guardrails for LLMs, and how do you implement them?
原题How do you implement content filtering for AI outputs?
原题How do you estimate the cost of running an AI-powered feature in production?
原题How do you optimize LLM inference costs in production?
原题How do you implement A/B testing for LLM systems?
原题What is CI/CD for AI applications, and how does it differ from traditional CI/CD?
原题How do you version and manage prompts in production?
原题What is model versioning, and how do you handle model rollbacks?
原题How do you implement rate limiting and throttling for LLM APIs?
原题How do you handle model updates and migrations without downtime?
原题What is the role of feature flags in AI deployments?
原题How do you implement logging and tracing for LLM applications?
原题How do you handle PII and sensitive data in LLM inputs and outputs?
原题What is a gateway pattern for LLM API management?
原题How does Token Streaming work?
原题How do you implement streaming responses for real-time AI applications?
原题How does vLLM work?
原题How does SGLang work?
原题How does TensorRT-LLM work?
原题How does llama.cpp run LLMs on everyday hardware?
原题How does Ollama work?
原题When would you choose vLLM vs SGLang vs TensorRT-LLM?
原题What are the key SLAs and metrics for production AI systems (latency, throughput, availability)?
原题Cloud vs on-device Model Deployment for AI applications.
原题How do you implement fallback strategies when the primary model is unavailable or rate-limited?
原题How do you implement structured output from LLMs reliably in production?
原题How do you handle long contexts efficiently in production (context compression, prefix caching)?
原题What is semantic routing, and how do you implement it in a multi-model system?
原题How do you manage secrets and API keys securely in LLM applications?
原题Your LLM API has latency spikes during peak hours. How do you stabilize it?
原题Your LLM endpoint's p99 latency doubled after a deploy with no model change. How do you diagnose it?
原题Your LLM costs are too high in production. How do you reduce costs without degrading quality?
原题Your application is hitting LLM provider rate limits during peak hours. How do you handle it?
原题Your application depends on one LLM provider. How do you switch providers without downtime?
原题Your AI system handles 100 requests/sec but crashes at 5000. How do you scale for concurrent requests?
原题A traffic spike brings down your AI system. How do you handle peak traffic?
原题One LLM provider outage took down your entire system. How do you eliminate single points of failure?
原题Your multi-LLM pipeline fails when one model in the chain breaks. How do you handle orchestration failure?
原题Your AI pipeline has zero visibility into which step is failing. How do you add observability?
原题You quantized your LLM, but accuracy dropped significantly. How do you minimize quantization loss?
原题One failing AI component can take down your entire platform. How do you design graceful degradation?
原题
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