Project notes
Probable Cause Data editing turns parsed OEM repair text into curated probable causes. Subject-matter experts used an Excel macro and a library mapping source text to previously applied edits. Its limited matching left unmatched text for manual drafting.
I built a workflow that looks for each input in that library, progressing through exact, case-insensitive, whitespace-normalized, formatting-normalized, and semantic matching. A match fills the corresponding prior edit into the output. Normalization preserves symbols with automotive meaning and exposes conflicting library mappings for expert review.
Inputs without a reusable match go to a separate generation step. This step uses a fine-tuned Flan-T5 Small model to generate new draft edits.
Matching and generation run as separate Cloud Run Jobs connected by Cloud Storage upload events. I also built a simple upload/download UI for non-technical experts. I designed and implemented the system independently apart from the UI's Kubernetes deployment, which a DevOps engineer owned.
The primary subject-matter expert estimated at least a 50% reduction in total editing effort.