Structured Output and Constrained Generation¶
LLMs can be constrained to produce output in specific formats through several mechanisms, reducing certain hallucination modes by restricting the space of possible outputs.
Mechanisms¶
- JSON mode: Restricts output to valid JSON. Prevents free-text narrative where structured data is expected
- Function calling schemas: Define expected structure of tool invocations including parameter names, types, and allowed values
- Grammar-constrained decoding: Enforces a formal grammar at the token-sampling level, rejecting tokens that would produce syntactically invalid output before they are selected
What It Solves¶
Schema enforcement addresses the syntactic dimension of errors: malformed tool calls, invalid JSON, wrong field types. A model constrained to produce valid JSON with a defined schema cannot hallucinate free-text where structured data is expected. It must place values in correct fields with correct types.
For tool-calling, schema enforcement ensures well-formed invocations rather than unparseable strings. This eliminates one class of tool-calling failure.
What It Does Not Solve¶
Constrained generation addresses syntax, not semantics. The model can produce a perfectly valid JSON object containing entirely fabricated values. A schema expecting a numerical field for reactor coolant temperature will receive a number — but that number may be hallucinated rather than derived from actual data.
The structure is correct; the content may not be.
Nuclear Application¶
For nuclear advisory, structured output provides a useful engineering layer that eliminates formatting errors. But validation of content accuracy requires the knowledge grounding mechanisms described in retrieval-augmented-generation and knowledge-graphs. Typed artefacts in the hsi-architecture use structured output to ensure agent responses follow consistent formats that operators can quickly parse.