The effectiveness of the retrieval stage in a Retrieval-Augmented Generation system is foundational. The precision of this stage, where relevant context is fetched, directly influences the performance of the subsequent generation phase. This chapter details advanced methods to enhance this component, ensuring your RAG system sources the most accurate and pertinent information.

You will learn to:

• Fine-tune embedding models for domain specificity.
• Implement hybrid search by combining dense and sparse retrieval methods.
• Utilize sophisticated re-ranking architectures to refine search results.
• Apply query augmentation techniques for improved understanding of user intent.
• Optimize chunking strategies for diverse data sources.
• Work with advanced document representations, such as multi-vector embeddings.
• Integrate knowledge graphs to enrich retrieval context.
• Use active learning for continuous retriever improvement.

By mastering these techniques, you will be able to significantly improve the relevance and accuracy of the information supplied to the generator in your RAG systems.