The Two Eval Loops Every Production LLM System Needs

1. 1Monitor traditional metrics such as latency, error rates, uptime, tokens, and cost
2. 2Implement the first eval loop to assess model performance and data quality
3. 3Implement the second eval loop to evaluate system reliability and robustness
4. 4Compare and analyze results from both eval loops to identify areas for improvement
5. 5Apply changes and re-evaluate system performance using the two eval loops

Data scientists, engineers, and product managers working on LLM systems will benefit from understanding the importance of eval loops in production environments to ensure system reliability and performance

💡 Traditional monitoring metrics are not enough, two eval loops are necessary to ensure production LLM systems are reliable and performant

💡 Two eval loops are crucial for production LLM systems: model performance & system reliability. Don't just monitor metrics, evaluate and improve!