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

2026-03-28

AI Summary

This work addresses the lack of actionable, deployment-ready evaluation mechanisms in current LLM/RAG applications, which hinders effective translation of offline metrics into informed deployment decisions. The authors propose an integrated readiness assessment framework that uniquely combines multidimensional indicators—including workflow success rate, compliance, factual consistency, retrieval hit rate, cost, and latency. By leveraging automated benchmarking, OpenTelemetry-based observability, CI quality gates, and Pareto frontier analysis, the framework generates scenario-weighted readiness scores to guide deployment choices. Empirical validation on FiQA, SciFact, and ticket-routing tasks demonstrates the framework’s ability to reliably distinguish model readiness levels and successfully intercept unsafe prompt variants within CI pipelines, thereby closing the loop from evaluation to deployment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• LLM readiness
• RAG applications
• deployment decision
• evaluation framework
• operational grounding

Innovation

• LLM Readiness
• RAG Evaluation
• CI Quality Gates
• Observability
• Pareto-Optimized Deployment