LLM Readiness Harness: Evaluation, Observability, and CI Gates for LLM/RAG Applications
For practitioners deploying LLM/RAG systems, this provides a reproducible, operationally grounded framework to make deployment decisions based on multi-metric readiness scores rather than single offline metrics.
The paper presents a readiness harness for LLM/RAG applications that combines automated benchmarks, OpenTelemetry observability, and CI quality gates to produce scenario-weighted readiness scores with Pareto frontiers. Evaluated on ticket-routing and BEIR grounding tasks (SciFact, FiQA) with full Azure matrix coverage (162/162 valid cells), the harness shows that readiness is multi-faceted: on FiQA under sla-first at k=5, gpt-4.1-mini leads in readiness and faithfulness, while gpt-5.2 incurs high latency; on SciFact, models are closer but separable; ticket-routing gates reject unsafe prompts, blocking risky releases.
We present a readiness harness for LLM and RAG applications that turns evaluation into a deployment decision workflow. The system combines automated benchmarks, OpenTelemetry observability, and CI quality gates under a minimal API contract, then aggregates workflow success, policy compliance, groundedness, retrieval hit rate, cost, and p95 latency into scenario-weighted readiness scores with Pareto frontiers. We evaluate the harness on ticket-routing workflows and BEIR grounding tasks (SciFact and FiQA) with full Azure matrix coverage (162/162 valid cells across datasets, scenarios, retrieval depths, seeds, and models). Results show that readiness is not a single metric: on FiQA under sla-first at k=5, gpt-4.1-mini leads in readiness and faithfulness, while gpt-5.2 pays a substantial latency cost; on SciFact, models are closer in quality but still separable operationally. Ticket-routing regression gates consistently reject unsafe prompt variants, demonstrating that the harness can block risky releases instead of merely reporting offline scores. The result is a reproducible, operationally grounded framework for deciding whether an LLM or RAG system is ready to ship.