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Production LLM evaluation and regression: an engineering setup guide.

LLM failure is silent. The model still responds, the answers look plausible, latency is in budget, tokens look normal, but quality has quietly degraded. Standard monitoring catches none of it. Five parts for a Python-native team, in order.

Field note / PlaybookAuthor: Vishal Shukla, VP of TechnologyCross-industry, regulatedRelevant to: AI Copilots · AI Security
Key takeaways
LLM failure is silent; standard monitoring catches none of it.
Five parts, in order: golden evaluation set, regression suite, automated evaluator models (LLM-as-Judge), drift detection, customer-facing handoff criteria.
Most teams build in the wrong order (regression suite first); the golden set is the foundation, build it first.
Under 3% hallucination on monitored queries is a defensible benchmark; handoff has seven pass criteria.
The setup

Five parts, in order. The golden set comes first.

Part 1

The Golden Evaluation Set

Foundation
Minimum

25–50 versioned input-output pairs defining "correct," in git, business-owned, tagged, reviewed quarterly.

Good looks like

Grown from ~25 to 300+ in the first year; evaluated automatically on every change.

Part 2

The Regression Suite

Enforcement
Minimum

Pytest-style (DeepEval, Braintrust); PR triggers a full run; pass/fail thresholds per metric, accuracy, faithfulness, relevancy, safety; a regression blocks merge; results posted to the PR.

Good looks like

The CI gate caught at least one regression in the first three months; new production failure patterns are added within a week.

Part 3

Automated Evaluator Models (LLM-as-Judge)

Scale layer
Minimum

A frontier model as judge with an explicit rubric, faithfulness, relevancy, safety, helpfulness, custom; calibrated against a human-scored subset (100 cases baseline); pairwise comparison. Known biases: verbosity inflation, self-preference, position effects.

Good looks like

Judge calibration documented; the open-source judge Prometheus (13B) reaches Pearson 0.897 with humans, on par with GPT-4's 0.882.

Part 4

Drift Detection

Early warning
Minimum

Sample 1–5% of traffic into an eval log; run LLM-as-Judge daily; alert on regression; track input metrics (query length, topic, segment) and output metrics (response length, refusal rate, citation rate).

Good looks like

Drift shown over the last 90 days; at least one drift event caught before users noticed; hallucination stays under 3%.

Part 5

Handoff Criteria for Customer-Facing

Gate
Minimum

Golden set stable 30 days without a failed regression; adversarial pass rate at or above threshold (prompt injection, jailbreak, extraction); production monitoring live with one test alert routed and acknowledged; an AI-specific incident response playbook rehearsed; staged rollout and a tested rollback path; legal and compliance sign-off on the shipping version.

Good looks like

All seven criteria documented as met; staged rollout starts at 1–5% and ramps on signal; the first week is staffed for incident response.

The stack we recommend in 2026
DeepEval, CI evaluationRAGAS, RAG-specificBraintrust, production traceabilityPromptfoo, prompt-levelPrometheus, open-source judgeOWASP LLM Top 10 / NIST AI RMF / MITRE ATLAS, adversarial corpora

Substitute LangSmith, TruLens, Vertex AI Studio, or Azure AI Foundry where they fit.

PDF

Get the 8-page guide

Includes the golden set template, regression scaffolding, judge rubrics, drift dashboards, and the handoff checklist.

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