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 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 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.
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