What problem does it solve? Interviewers hiring for LLM application engineering roles often struggle to design questions that distinguish real production experience from tutorial-level projects, especially in fast-moving areas like RAG, agents, and fine-tuning. ## Core Features & Use Cases - Topic Coverage: Provides structured question ladders across RAG pipelines, embedding retrieval, agent architecture, MCP tool governance, prompt engineering, context management, fine-tuning, inference optimization, evaluation systems, safety, and cost control. - Signal Detection: Each topic lists danger signals and expected signals so interviewers can score answers consistently and probe resume claims like "accuracy improved X%" or "built a RAG system". - Use Case: When a candidate's resume mentions an Agent project, use this Skill to generate follow-up questions about tool counts, termination conditions, token budgets, and past incidents to verify depth of experience. ## Quick Start Generate interview questions for a candidate whose resume mentions a RAG project and LangChain experience.
