Back to LLM / GenAI & Prompt/Context Engineering
About this interview
A technical interview on Structured Output and Function Calling, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain JSON-mode and grammar/constrained decoding for reliable structured output
Describe function/tool-call schemas and how the model selects and fills them
Explain reliability pitfalls (hallucinated args, schema drift) and mitigations
Reason about when structured output suffices vs needing a full agent loop
Topics covered
Structured Output BasicsJSON ModeJSON Mode vs SchemaTool Call SchemasTool Call Message FlowConstrained DecodingGrammar-Based GenerationParallel vs Sequential Tool CallsTool Choice ControlArgument HallucinationArgument ValidationSchema DriftStructured Output LibrariesRetry and Validation Patterns
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In plain terms, what does it mean to get structured output from an LLM, and why does raw free-text output fall short in production systems?
“What is schema drift, and why is it a reliability problem for long-running LLM-powered applications that depend on function calling?
“What is the difference between a tool-calling model and a model that has been fine-tuned to emit function-call tokens natively? Why does the distinction matter for deployment?