A provider quietly updated the base model. The API is formally the same, the version string is unchanged \u2014 but behavior shifted: Kovcheg started inventing loan terms not present in the documents slightly more often, and treating applications from one region slightly more strictly. No one changed anything in the code. A month later it surfaces as a spike in complaints and a question from compliance: "Why have approvals in region N dropped?" This is what silent degradation looks like \u2014 the most common way an AI system's quality erodes without a single deployment.<\/p>\n
<\/p>\n
This chapter covers the quality plane<\/strong>: the guarantee that the model doesn't degrade over time, answers stay accurate through base-LLM updates, and decisions don't discriminate. For a credit-scoring system like Kovcheg, bias in scoring isn't a dashboard metric. It's a direct violation of the EU AI Act (Art. 10 \u2014 data quality, non-discrimination) and a legal claim.<\/p>\n The quality-plane engineer builds the evaluation infrastructure that makes these guarantees measurable and enforces them as a release gate.<\/p>\n Kovcheg's decision quality and fairness must be measurable, continuously monitored, and wired into the release pipeline. Three promises to the business:<\/p>\n Continuous Evaluation & LLM-as-a-Judge Pipeline.<\/strong> Evaluation is a continuous process: regression against golden datasets in CI\/CD, online RAG metrics, drift monitoring, and fairness tests \u2014 all backed by an honestly calibrated judge.<\/p>\n Reference sets for regression-testing prompts and models in CI\/CD. A model or prompt update can't ship without passing the run. The dataset includes "hard" cases and cases pulled from real incidents.<\/p>\n On a sample of production traffic: Faithfulness<\/strong> (grounding to context \u2014 a direct defense against the hallucinations from the opening scenario), Answer Relevance<\/strong>, Context Recall\/Precision<\/strong>. This ties to the output guardrail from Chapter 3 \u2014 that's the real-time block; this is the trend.<\/p>\n Anomalies in the distribution of incoming query embeddings (data drift) and in answer quality (concept drift). A shift triggers an alert. One subtlety: semantic caching (Chapter 5) masks drift \u2014 measure on the uncached sample.<\/p>\n A regular run of synthetic tests: swap protected attributes (gender, age, region) while holding everything else equal, then compare decisions. A systematic difference is a bias signal. For Kovcheg, this is a mandatory gate for the credit module.<\/p>\n Metrics are part of the pipeline (policy-as-code, Chapter 6). Falling below a threshold blocks the deploy:<\/p>\n Build a golden dataset for Kovcheg (including incident-derived cases), configure Ragas metrics online, set up Evidently for embedding drift on the uncached sample, implement an attribute-swap fairness test for the credit module, wire an eval gate into CI, calibrate the judge against human labeling, and measure agreement.<\/p>\n Artifact<\/strong>: an eval suite + a quality\/drift dashboard + a fairness report + a judge-calibration report (evidence for the RMS, Chapter 6).<\/p>\n The provider will update the base model again next quarter \u2014 the version string won't change, and the behavior will shift. The question is whether your eval gate catches it before compliance does.<\/p>\n","protected":false},"excerpt":{"rendered":" A provider quietly updates the base model. The API is unchanged, behavior shifts, and no one deployed a thing. Continuous evaluation and LLM-as-a-judge are how you catch it before compliance asks why approvals in region N dropped.<\/p>\n","protected":false},"author":0,"featured_media":185,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[147],"tags":[150,189,188,187,190,186],"class_list":["post-186","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-governance","tag-ai-governance","tag-bias","tag-fairness","tag-llm-evaluation","tag-llm-as-a-judge","tag-model-drift"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\tThe business goal<\/h2>\n
\n
What drives this<\/h2>\n
\n
The architectural pattern<\/h2>\n
Engineering stack<\/h3>\n
\n
Engineering implementation<\/h2>\n
Step 1. Golden datasets<\/h3>\n
Step 2. Online RAG metrics<\/h3>\n
Step 3. Drift monitoring<\/h3>\n
Step 4. Fairness and bias<\/h3>\n
Step 5. Release eval gate<\/h3>\n
eval_gate:\n faithfulness: {min: 0.92}\n answer_relevance: {min: 0.85}\n fairness_delta: {max: 0.03} # max. difference in decisions on attribute swap\n block_release_on_fail: true<\/code><\/pre>\nWhere it breaks<\/h2>\n
\n
Standards and mapping<\/h2>\n
\n
Lab and artifact<\/h2>\n
Maturity checklist<\/h2>\n
\n
Sources<\/h2>\n
\n