16 Core Eval and Deployment Practices for Production-Grade LLM Apps
twiecki · x · 2026-08-05
The original tweet outlines the essential evaluation and engineering systems needed before deploying LLM applications in production. Key points include:
- Eval Data & Metrics: Create golden datasets with 100+ test cases, implement LLM-as-a-judge with calibrated scoring, and track hallucination rates and citation validation beyond exact match.
- Safety: Add safety evaluations for toxic and biased outputs.
- Deployment & Monitoring: Utilize A/B testing for prompts, shadow deployments, and canary releases with automatic rollback. Track latency percentiles (p50, p95, p99).
- Model Selection & Ops: Build cost-quality tradeoff curves for model selection, and establish alerting and dashboards to monitor quality trends and report to stakeholders.
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