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PRANJUL RATHOUR
PRANJUL RATHOUR

Posted on Originally published at pranjulrathour.scult.in

Fine-tuning for reliable structured output (JSON mode isn't always enough)

#finetuning #structuredoutput #json

Prompt-based JSON mode gets a model to output valid JSON most of the time, but 'most of the time' isn't good enough for a pipeline that parses the output programmatically at any real volume.

When fine-tuning helps here

  • A fixed, well-defined schema used repeatedly — fine-tuning teaches the exact shape far more reliably than repeating it in every prompt.
  • High request volume, where even a small failure rate produces a meaningful number of broken parses per day.
  • A schema complex enough that prompting alone produces inconsistent field names or nesting.

Building the dataset

Pairs of input and the exact target JSON output, including edge cases (empty fields, optional values) that prompting alone tends to handle inconsistently. Validate every training example against the schema before training — a malformed training example teaches the wrong lesson.

See fine-tuning dataset formats and chat templates.

About Pranjul Rathour

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Requirements gathering, on stage

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Pranjul Rathour

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Pranjul Rathour, GenAI engineer from Kanpur, in a white turtleneck and black jacket, looking to the side
Pranjul Rathour — GenAI engineer, Kanpur

Pranjul Rathour is a GenAI engineer from Kanpur, India, and CTO at SCULT INDIA, currently shipping production RAG,
fine-tuning and agentic AI systems, mentoring 200+ students through TechVerse Enclave, and judging and speaking at
student hackathons across India. Updated 2026-09-11.

Reach out if you want to talk GenAI, book a campus session, or invite him to judge:

Pranjul Rathour · GenAI engineer, 3x hackathon winner, campus mentor. Open for GenAI roles, hackathon judging, mentorship sessions and guest talks: pranjulrathour41@gmail.com · Invite me to your campus
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