Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
Cloud & Infrastructure
The New Stack·Jul 2·5 min read
Why traditional CI/CD fails for LLMs (and the release gates we built to fix it)
TL;DR
Traditional Continuous Integration/Continuous Deployment (CI/CD) processes fail for Large Language Models (LLMs) due to undetected regressions. A new release-gating approach was developed, incorporating baseline evaluations, drift detection, shadow validation, and cost/latency guardrails.
✦ Why It Matters
Engineers can adopt this release-gating approach to prevent silent regressions in AI systems, ensuring better performance and reliability.
Key Takeaways
- Adopt a baseline evaluation suite to catch regressions early.
- Implement drift detection to monitor gradual performance changes.
- Use shadow traffic validation for real-world performance comparison.
- Ensure cost and latency checks are part of the deployment process.
How It Works
The release-gating framework consists of four gates: a baseline evaluation suite that scores model performance, drift detection that compares current scores against a rolling baseline, shadow traffic validation that routes a small percentage of real user traffic to the candidate model for comparison, and cost/latency checks that enforce operational budgets. This multi-faceted approach allows for continuous monitoring of model behavior, ensuring that any degradation is caught before reaching users.