Building a Multi-Agent RAG System with a Self-Improving Eval Loop
📰 Medium · AI
Learn to build a multi-agent RAG system with a self-improving eval loop to move LLMs to production
Action Steps
- Build a multi-agent RAG architecture using a structured orchestration approach
- Implement an automated self-improving eval loop to refine model performance
- Configure the eval loop to collect feedback and update model parameters
- Test the multi-agent RAG system with various input scenarios
- Apply the self-improving eval loop to optimize model performance over time
Who Needs to Know This
This guide is beneficial for AI engineers and researchers working on LLMs and RAG systems, as it provides a framework for moving models to production and improving their performance over time.
Key Insight
💡 A self-improving eval loop is crucial for refining model performance and moving LLMs to production
Share This
🤖 Build a multi-agent RAG system with a self-improving eval loop to move LLMs to production! 🚀
Key Takeaways
Learn to build a multi-agent RAG system with a self-improving eval loop to move LLMs to production
Full Article
A guide to moving LLMs to production using a multi-agent RAG architecture, structured orchestration, and an automated self-improving loop. Continue reading on Medium »
DeepCamp AI