LLM evals cheat sheet: what to check, how, and when
A one page guide to evaluating LLM and agent systems: code checks, rubrics, model judges, eval sets and when to run them.
Interview question banks, learning paths and concept explainers for engineers who build AI systems for real customers.
Interview questions20 RAG and LLM evals interview questions for AI engineers, with what a strong answer covers: retrieval, chunking, re-ranking, regression evals, monitoring.
25 forward deployed engineer interview questions grouped by what they test, with what a strong answer covers: discovery, data access, evals, agents, handover.
A one page guide to evaluating LLM and agent systems: code checks, rubrics, model judges, eval sets and when to run them.
Common system design questions for AI engineer and forward deployed engineer interviews, and what a strong answer covers for each.
20 RAG and LLM evals interview questions for AI engineers, with what a strong answer covers: retrieval, chunking, re-ranking, regression evals, monitoring.
A step by step learning path for the forward deployed engineer role: model APIs, retrieval, evals, agents and tools, data access, domain knowledge and handover.
25 forward deployed engineer interview questions grouped by what they test, with what a strong answer covers: discovery, data access, evals, agents, handover.
A question bank for agentic AI engineering interviews: workflows versus agents, the five common patterns, tool design, human checkpoints and testing.