RAG in Action: Beyond Basics to Advanced Data Indexing Techniques

Author(s): Ryan Nguyen

Originally published on Towards AI.

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Document Hierarchies, Knowledge Graphs, Advanced Chunking Strategies, Multi Retrieval, Hybrid Search, Reranking, Trade-offs and more

Some time ago, I wrote an article on enhancing your RAG pipeline and outlined seven strategies for fine-tuning LLM. These techniques and strategies have proven effective in elevating the overall performance of the RAG pipeline.

Ways to go from prototype to production with LlamaIndex

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In this article, I aim to dig into additional technical considerations for RAG implementation. Beyond the revisited chunking technique, I will introduce other methods, including query augmentation, hierarchies, and an intriguing element I’ve recently explored: knowledge graphs. I will also explore unsolved challenges and opportunities within the RAG infrastructure space, providing potential solutions for these issues.w

Back on the first day of 2023, my primary focus centred on Vector DB and its performance within the broader design landscape. However, as we approach the conclusion of 2023, significant developments have unfolded in this domain. In the design of a RAG system, I consider a few things.

The ongoing battle in the realm of LLM models between open-source and closed-source. What is the best model for my pipeline?Should I need to fine-tune LLM or embed the model for the dataset?Secondly, the evolution of document processing strategies has… Read the full blog for free on Medium.

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Published via Towards AI