Mastering RAG: Advanced Retrieval Techniques

March 27, 2024 1:00 PM ET | 10:00 AM PT

Mastering RAG: Advanced Retrieval Techniques

Building on the "Introduction to RAG" tech hour, this next session in the series, "Advanced Retrieval-Augmented Generation (RAG)," is designed for those who have a foundational understanding of RAG concepts and are ready to delve deeper into more complex and sophisticated RAG technologies. We embark on this exploration by dissecting the foundational principles of Naive RAG, Advanced RAG, and a glimpse into the Modular RAG, set to be discussed in a subsequent session. Each of these methodologies is scrutinized for their unique approaches to integrating external knowledge into LLMs, thereby enriching the model's understanding and response accuracy.

In the journey through the intricate landscape of Advanced RAG, we commence with the Pre-retrieval phase, where the emphasis is on Query Routing and Query Rewriting. The former navigates through the maze of available data sources to pinpoint the most suitable one that can aid the LLM in fulfilling the user's request with precision. The latter, Query Rewriting, is an art in itself, aiming to refine and elevate the user's query to fetch results that surpass the original expectations, setting a new benchmark for user satisfaction.

Transitioning into the Retrieval phase, we explore the dynamic and multi-faceted nature of document retrieval strategies. Dynamic Document Retrieval stands at the forefront, showcasing its agility in adapting to the query's context and nuances to procure the most relevant documents. Complementing this is the Multi-Source Retrieval strategy, which broadens the horizon by amalgamating information from a plethora of sources, thereby enriching the context and depth of the generated content.

As we venture into the Post-Retrieval phase, the focus shifts to Re-ranking, a critical process that fine-tunes the relevance and quality of the retrieved information, ensuring that the output resonates with the user's needs and expectations.

Lastly, the presentation underscores the importance of Keeping it Fresh, a mantra for maintaining the currency and relevance of the knowledge base. This is achieved through regular data ingestion Domino Job processes, which ensure that the model remains updated with the latest information, thereby preserving its edge in delivering accurate and timely responses.

Join us in this enlightening session as we unravel the complexities of RAG techniques and their transformative impact on the future of LLMs, setting new paradigms in artificial intelligence and machine learning.