Dataford
Interview QuestionsInterview GuidesExperiencesMock InterviewsPricing
Get started
Dataford
Popular roles
Software EngineerData AnalystData ScientistData EngineerBusiness AnalystAI EngineerMachine Learning EngineerProduct Manager
Browse
Browse All RolesEvery role hub, from analyst to MLBrowse All CompaniesCompany-specific interview loopsAll Interview GuidesThe full guide library
Top questions by role
Software EngineerData AnalystData ScientistData EngineerBusiness AnalystAI EngineerMachine Learning EngineerProduct Manager
Top questions by skill
SQLPythonStatisticsMachine LearningA/B TestingSystem DesignGenerative AIProduct SenseMetricsBehavioral
Browse all questions →Try a mock interview
Experiences
Practice
Mock InterviewsTimed interview simulations with feedbackSuccess PathYour 6-week structured planModulesCurated lessons by topicWebinarsTalks from ex-Big Tech data leadsPlaygroundA free-form scratch editor
Learn
BlogInterview strategy and career adviceTech Job Market ReportHiring trends across data and AI rolesFor UniversitiesDataford for career centersAbout DatafordWho we are and how we build
Pricing
Build my plan

LLM Evaluation Metrics

HardGenerative AI & LLMs00:00
Practice interviewer
In session
5 left
00:00

Your question is LLM Evaluation Metrics. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

You need to log in / sign up to chat or submit.

Problem

How do you approach LLM evaluation? What metrics do you prioritize for a summarization task versus a Q&A task?

Explain how you would build an evaluation strategy rather than relying on a single score. Cover reference-based metrics, rubric-based human or LLM evaluation, hallucination and faithfulness checks, refusal behavior, latency, cost, and production monitoring. Distinguish metrics that measure summary quality from those that measure answer correctness and grounding.

Provide a practical evaluation design, including representative test data, error analysis, and release criteria.