00:00 - Introduction by Jerry, co-founder of llama index 07:32 - Overview of building a knowledge assistant using RAG 16:45 - Components of RAG: data parsing and ingestion, data querying 27:18 - Challenges with naive RAG systems 35:40 - Importance of data quality in RAG systems 43:12 - Improving data quality through data processing 51:30 - Improving query complexity in RAG systems 58:45 - Goal of building a more advanced research assistant 01:05:20 - Conclusion and future directions of RAG development
I believe what he is suggesting is you extract key bits of information from the document as specific items (for example the title, or even just individual sentences) and then feed this to the LLM alongside the raw text. I assume this is to give the LLM some additional clues about the structure of the data. Its almost like adding some kind of a markup on the document alongside the raw data.
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00:00 - Introduction by Jerry, co-founder of llama index
07:32 - Overview of building a knowledge assistant using RAG
16:45 - Components of RAG: data parsing and ingestion, data querying
27:18 - Challenges with naive RAG systems
35:40 - Importance of data quality in RAG systems
43:12 - Improving data quality through data processing
51:30 - Improving query complexity in RAG systems
58:45 - Goal of building a more advanced research assistant
01:05:20 - Conclusion and future directions of RAG development
20:50 Im not sure I understand how to do this, can someone explain, please?
I believe what he is suggesting is you extract key bits of information from the document as specific items (for example the title, or even just individual sentences) and then feed this to the LLM alongside the raw text. I assume this is to give the LLM some additional clues about the structure of the data. Its almost like adding some kind of a markup on the document alongside the raw data.