LLM Evaluation & Ops
Getting an LLM to work in a demo is easy. Getting it to work reliably in production — at scale, across edge cases, through model updates — is the real engineering challenge.
LLM evaluation and ops questions test your ability to build the infrastructure around AI models: how you measure quality, how you detect regressions, how you manage model versions, and how you balance the competing constraints of latency, cost, and output quality.
Companies that ship AI products well treat LLM evaluation like software testing: systematic, automated, and integrated into the deployment process. Candidates who understand this stand out.
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LLM Evaluation & Ops Interview Questions
Explain the Tradeoffs Between Latency, Cost, and Quality in LLM Selection
Navigate the three-way tradeoff between LLM latency, cost, and quality — and learn how to make the right selection for different use cases.
Read questionWhat Metrics Would You Track for an LLM in Production?
A comprehensive framework for monitoring LLMs in production — from latency and cost to output quality and user satisfaction signals.
Read questionHow Do You Build an Eval Suite for an LLM-Powered Feature?
Walk through building a systematic evaluation suite for an LLM feature — from test case design to automated metrics and regression tracking.
Read questionHow Do You Evaluate a RAG System End-to-End?
RAG evaluation is distinct from general LLM evaluation — it requires measuring both retrieval quality and generation quality independently and together. Walk through the key metrics and frameworks.
Read questionHow Would You Detect and Handle LLM Output Regressions?
Build a system to detect when LLM output quality degrades — covering statistical monitoring, automated quality checks, and incident response.
Read questionHow Do You Optimize LLM Inference for Higher Throughput and Lower Latency?
Walk through the key techniques for optimizing LLM inference performance in production — KV cache management, quantization, continuous batching, and speculative decoding.
Read questionHow Do You Handle Model Version Upgrades Without Breaking Production?
A safe, systematic approach to upgrading LLM model versions in production — from pre-upgrade evaluation to canary deployment and rollback.
Read questionPrep for the full interview loop
Know the concepts. Now prove it. Practice GenAI, Coding, System Design, and AI/ML Design interviews with an AI that tells you exactly where you fell short.
Start a mock interviewFrequently Asked Questions
How do you evaluate an LLM in production?▾
Production LLM evaluation requires multiple layers: (1) Offline eval — benchmarks and test sets run before deployment; (2) Online eval — live traffic sampling with human or LLM-as-judge scoring; (3) Monitoring — tracking latency, cost, error rates, and output quality metrics. Key metrics include relevance, faithfulness, answer completeness, and hallucination rate. Tools like RAGAS, PromptFoo, and custom LLM-as-judge pipelines are commonly used.
What LLM evaluation questions come up in AI interviews?▾
Common LLM eval interview questions: building an eval suite from scratch, detecting output regressions during model upgrades, LLM-as-judge vs human evaluation tradeoffs, measuring hallucination rates, evaluating RAG retrieval and generation separately, latency/cost/quality tradeoff analysis, and designing A/B testing frameworks for LLM outputs.
What does LLMOps mean and what does it involve?▾
LLMOps covers the operational practices for running LLM-powered systems in production: model versioning and safe rollouts, prompt versioning and A/B testing, cost monitoring (token usage and spend), latency tracking, output quality monitoring, incident response for hallucinations or failures, and feedback loops that improve the system over time. It's the LLM equivalent of MLOps.