LLM Readiness Harness: Evaluation, Observability, and CI Gates for LLM/RAG Applications
A recent paper, arXiv:2603.27355, presents a readiness harness tailored for LLM and RAG applications. This innovative system integrates automated benchmarks, OpenTelemetry for monitoring, and CI quality gates into the decision-making process for deployments. It assesses key metrics like workflow success, adherence to policies, groundedness, retrieval hit rate, costs, and p95 latency to create scenario-weighted readiness scores with Pareto frontiers. Testing on ticket-routing workflows and BEIR grounding tasks, including SciFact and FiQA, across all 162 Azure matrix cells showed that readiness has many dimensions. For example, under sla-first at k=5, gpt-4.1-mini performed well in readiness and reliability, while gpt-5.2 struggled with latency. Additionally, the ticket-routing regression gates effectively filter out unsafe prompts.
Key facts
- arXiv:2603.27355 introduces a readiness harness for LLM and RAG applications.
- The harness combines automated benchmarks, OpenTelemetry observability, and CI quality gates.
- Metrics include workflow success, policy compliance, groundedness, retrieval hit rate, cost, and p95 latency.
- Readiness scores are scenario-weighted with Pareto frontiers.
- Evaluation covers ticket-routing workflows and BEIR grounding tasks (SciFact and FiQA).
- Full Azure matrix coverage of 162/162 valid cells across datasets, scenarios, retrieval depths, seeds, and models.
- On FiQA under sla-first at k=5, gpt-4.1-mini leads in readiness and faithfulness.
- gpt-5.2 pays a substantial latency cost on FiQA.
- On SciFact, models are closer in quality but still separable operationally.
- Ticket-routing regression gates consistently reject unsafe prompts.
Entities
Institutions
- arXiv
- Azure
- OpenTelemetry