Ask an LLM to summarise a contract and it does a reasonable job. Ask it to give you a compliance score you'll act on, and run the same document through it twice, and you may get two different scores. That inconsistency is fine for a summary and unacceptable for anything downstream of it.
Separate extraction from scoring
In DocuLens AI, the LLM's job stops at extracting structured fields — vendor, amount, date, line items. A schema-validated, deterministic layer then scores those fields against defined rules. The LLM's variability is contained to the part of the pipeline that can tolerate it.
Schema validation is not optional
If the extracted output doesn't match a defined schema, reject it and retry or flag it — don't let malformed output silently propagate into a compliance score someone will act on.
The rule of thumb
Let the LLM read. Let deterministic code decide. The moment a decision needs to be reproducible and explainable, it shouldn't be resting on a single LLM call's non-deterministic output.
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.
