Level Up Your GenAI Apps: Overview of Advanced RAG Techniques

The second post in the series on advanced Retrieval-Augmented Generation (RAG) techniques delves into enhancing retrieval methods to improve the performance and accuracy of RAG systems. It highlights the limitations of basic chunking and semantic search when dealing with complex queries or ambiguous terms and emphasizes the importance of data preprocessing as a foundation for successful RAG implementation. The post explores various retrieval techniques such as re-ranking, which involves a second evaluation of retrieved content for relevance, and hybrid search, which combines vector and keyword-based searches like BM25 to capture semantic meaning and exact matches. Additionally, it discusses metadata pre-filtering to narrow down search space and improve precision, parent-document retrieval to balance chunk size and context, and query transformation techniques like Hypothetical Document Embeddings (HyDE) and query rephrasing to refine search queries. The post also introduces advanced approaches such as GraphRAG, which uses a knowledge graph for structured retrieval, and Agentic RAG, which employs LLM-based agents for dynamic, strategy-driven retrieval steps. Throughout, the emphasis is on the necessity of high-quality data preprocessing to ensure optimal retrieval outcomes.