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LLM & Agentic

Structured Output & Schema Enforcement

Structured output is the practice of constraining LLM responses to conform to a predefined schema — JSON, XML, or typed objects. This is critical for production systems where downstream consumers (APIs, databases, UIs) expect deterministic formats. The challenge: LLMs are fundamentally text generators, not schema-aware serializers.

Interview Tip: Interviewers love asking "How do you ensure an LLM always returns valid JSON?" The answer is never just "ask nicely in the prompt." You need to discuss multiple layers: prompt engineering, constrained decoding, validation, and retry strategies. Show you understand the tradeoffs between each approach.

JSON Mode vs Tool Use for Structured Output

There are two primary API-level approaches to structured output from major LLM providers: JSON Mode (OpenAI) and Tool Use / Function Calling (both OpenAI and Anthropic). They differ in mechanism, reliability, and flexibility.

OpenAI JSON Mode

OpenAI's JSON mode guarantees the response is valid JSON (syntactically), but does not guarantee it matches your desired schema. You must still validate the structure yourself.

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