LLM Infrastructure & Evals
Performs rigorous audits of LLM-powered features to ensure model currency, prompt quality, eval coverage, and observability.
This skill provides a comprehensive framework for maintaining high-quality, production-ready LLM implementations. It automates the audit of model versions by mandating real-time research to avoid stale training data, enforces best practices in prompt engineering through the Role-Objective-Latitude pattern, and integrates robust evaluation suites using tools like Promptfoo. By standardizing observability and CI/CD integration, it ensures that your AI features are properly instrumented, secure against injections, and guarded against regressions in cost or performance.
주요 기능
01Prompt quality auditing to replace over-prescriptive instructions with goal-oriented patterns
023 GitHub stars
03Real-time model currency research to identify the latest SOTA versions
04Security red teaming for prompt injection and jailbreak resistance
05Automated evaluation suite setup with CI/CD gating using Promptfoo
06Full-stack observability instrumentation for tracing and cost monitoring
사용 사례
01Migrating legacy LLM implementations to modern flagship models with proper versioning
02Implementing production-grade tracing and token usage monitoring for AI features
03Setting up automated testing and 'golden' datasets for prompt changes