Combining RAG Approaches

Welcome to the exciting world of combining RAG approaches! In this chapter, you'll dive into merging retrieval and generation components into a single pipeline. We’ll explore different methods such as chaining components, combining information from multiple sources, rating answers based on quality, and mixing these techniques.

You will discover the pros and cons of each approach, helping you choose the best strategy for your application. For instance, chaining components can provide quick results but might not always offer the most accurate answer. On the other hand, combining all available information gives a broader perspective but may overwhelm the language model with too much data.

We’ll also look at rating answers to prioritize quality over quantity and discuss how you can mix these methods for even more flexibility. By the end of this chapter, you'll have a solid understanding of how to integrate various RAG approaches into your AI application pipeline.

Ready to build smarter, more efficient AI systems? Let's dive in!