Optimizing RAG Applications

Thanks to LLM APIs, building a basic AI-powered application is easy. However, making it production-quality is quite challenging. In this tutorial series, we will explore ways to enhance the quality of Retrieval-Augmented Generation (RAG) applications. In each part of the series, we will cover one method with detailed explanations and code examples.

Here are the parts we cover and will cover in the future:

• Improved parsing and chunking
• Metadata filtering
• Hybrid search
• Advanced retrieval
• Small to big retrieval
• Reranking
• Embedded tables
• Multi document agents
• Routing
• Query planning
• Embedding Fine tuning
• LLM Fine tuning