Structured Output: JSON, Grammars, and Repair Loops

Local models can produce useful structured data, but students need grammars, schema checks, and repair loops. In local AI, the model family is only one part of the system. The runtime, file format, serving path, hardware budget, evaluation set, and safety policy decide whether the model becomes useful.

Current source signal

Build the small version

Ask a local model to output a flat JSON object, validate it, then run a repair prompt only if validation fails.

1. 1Define the user task in one sentence.
2. 2Choose the smallest model and runtime that might pass that task.
3. 3Run one happy-path prompt and one failure-path prompt.
4. 4Record speed, memory pressure, output quality, and the exact reason for any failure.
5. 5Write the operating rule you would give a non-expert user.

json_pipeline:
  prompt_with_schema
  generate
  strip_code_fences_if_needed
  parse_json
  validate_schema
  if invalid:
    repair_with_error_message
  store_only_valid_object

The big idea: validate then trust. A local model app is not done when the model answers once; it is done when the whole workflow can be installed, measured, trusted, and recovered.