Building Controllable, Debuggable LLM Workflows at Scale

1. 1Design a layered orchestration architecture for LLM workflows
2. 2Implement persistent memory to store model states and intermediate results
3. 3Configure selective forgetting to manage model complexity and reduce memory usage
4. 4Train and deploy 14B local models to improve model accuracy and reduce latency
5. 5Test and debug LLM workflows using logging and monitoring tools

Machine learning engineers and data scientists can benefit from this approach to improve the scalability and reliability of their LLM workflows, while product managers can use it to inform their product strategy

💡 A layered orchestration architecture with persistent memory, selective forgetting, and local models can improve the scalability and reliability of LLM workflows

🚀 Build controllable and debuggable LLM workflows at scale with layered orchestration architecture! 💡