The RAG Playbook
When it comes to building and improving Retrieval-Augmented Generation (RAG) systems, too many teams focus on the wrong things. They obsess over generation before nailing search, implement RAG without understanding user needs, or get lost in complex improvements without clear metrics. I've seen this pattern repeat across startups of all sizes and industries.
But it doesn't have to be this way. After years of building recommendation systems, instrumenting them, and more recently consulting on RAG applications, I've developed a systematic approach that works. It's not just about what to do, but understanding why each step matters in the broader context of your business.
Here's the flywheel I use to continually infer and improve RAG systems:
- Initial Implementation
- Synthetic Data Generation
- Fast Evaluations
- Real-World Data Collection
- Classification and Analysis
- System Improvements
- Production Monitoring
- User Feedback Integration
- Iteration
Let's break this down step-by-step: