LLM Readiness Harness: Evaluation, Observability, and CI Gates for LLM/RAG Applications

TL;DR

This paper introduces a comprehensive readiness harness for LLM and RAG applications that integrates evaluation, observability, and CI gates to support deployment decisions based on multiple operational metrics.

Contribution

It presents a novel framework combining automated benchmarks, observability, and quality gates into a unified workflow for assessing LLM/RAG readiness.

Findings

Readiness scores effectively differentiate model performance and operational suitability.

The harness can identify unsafe prompt variants and prevent risky releases.

Evaluation on multiple datasets shows the system's robustness and comprehensive coverage.

Abstract

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.