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.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
