LLM System Reliability

In regulated environments, finance, healthcare, legal, a silent LLM failure isn't just a product bug. It's a compliance incident. Most AI teams catch model drift in post-mortems, after a user reports something wrong. This project builds the tooling to catch it before it reaches users: a Python toolkit for measuring LLM reliability in production, covering drift detection across model updates, regression testing harnesses, and failure mode classification with severity scoring calibrated to regulated-industry tolerances.

1. 1.
   Drift Monitoring: Build detection tooling that flags statistically significant changes in model output behaviour across software updates, prompt changes, or underlying model version switches.
2. 2.
   Regression Test Harnesses: Create reproducible evaluation harnesses that run on every deployment and catch regressions against a curated set of high-stakes queries.
3. 3.
   Failure Mode Classification: Develop a taxonomy of LLM failure types, hallucination, refusal drift, confidence miscalibration, format degradation, with severity scores calibrated to regulated industry impact.
4. 4.
   Audit-Friendly Reporting: Produce structured, timestamped reliability reports that can be included in compliance documentation and model governance reviews.

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  Failure Mode Research: Catalogued 18 distinct LLM failure patterns from production incident logs and academic literature, then ranked by severity in regulated environments.
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  Baseline Capture: Built tooling to snapshot model behaviour across a standardised prompt battery at each deployment, creating the baseline against which drift is measured.
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  Statistical Drift Detection: Implemented drift detection using output distribution comparison across prompt categories, flagging shifts above configurable significance thresholds.
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  Severity Calibration: Worked backwards from compliance incident definitions to assign severity scores, a confidence miscalibration in a legal context scores higher than the same failure in a consumer setting.

Consumer AI reliability is usually measured by user satisfaction. Regulated industry reliability is measured by whether the output would survive regulatory scrutiny. Those are very different standards. A model that confidently answers 'no adverse events found' when it actually hallucinated a clean record isn't just unhelpful, it's a liability. The severity scoring in this toolkit is calibrated to that standard, not to CSAT metrics.

The regression harness is built around 'golden queries', a curated set of prompts where the correct output is known and agreed upon by domain experts. On each deployment, the harness re-runs every golden query and diffs outputs against the stored baseline. Changes above a semantic similarity threshold trigger a review gate. The golden query set is version-controlled and treated as a first-class product artifact.

This is a product reliability problem wearing engineering clothes. I designed the evaluation criteria from user impact backward, not from what the model metrics made easy to measure, but from what a failure would actually cost a compliance officer or a financial analyst.

A practical reliability layer that surfaces LLM degradation before it becomes a user-facing or compliance problem.