The LLM Didn't Win Everywhere — and That's What Made the Project Interesting
I closed an important stage of ops-triage-ai , an operational triage system that combines a deterministic baseline, a local LLM, and a hybrid policy with human review. The most interesting result was not simply "the LLM was better." What the benchmark measured Evaluation on a frozen held-out set of 70 synthetic tickets, in a single run. Deterministic baseline versus the local LLM: Category…
The LLM's triumph was not universal in the ops-triage-ai project, which introduced a novel approach to combining a deterministic baseline, a local LLM, and a hybrid policy with human review. The benchmark, conducted on a synthetic set of 70 tickets, demonstrated that while the LLM outperformed the baseline in certain categories, such as HIGH/CRITICAL priority recall, it still fell short overall, with a risk accuracy of 91.4% compared to the deterministic baseline's 95.7%.
The project's key takeaway was that a useful AI system need not blindly trust the model, but rather should understand when it performs best and where it falters. The final architecture featured a deterministic baseline as reference and fallback, a local LLM for semantic interpretation, a hybrid policy to determine human review necessity, and an audit trail recording all critical information.
Despite synthetic dataset constraints and a single official run, the system proved that combining different behaviors can yield a safe, observable, and auditable solution.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
