Summary of Observations on Building Rag Systems For Technical Documents, by Sumit Soman et al.
Observations on Building RAG Systems for Technical Documents
by Sumit Soman, Sujoy Roychowdhury
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate the limitations of retrieval augmented generation (RAG) in creating technical documents. They analyze existing works highlighting key factors impacting RAG’s performance and conduct experiments to identify best practices and pitfalls. This study aims to inform the development of effective RAG systems for technical documentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is important because it helps create accurate and relevant technical documents using AI. The authors look at what others have done, and then test different approaches to find out what works well and what doesn’t. By understanding these challenges, they hope to make better AI tools for writing technical documents. |
Keywords
* Artificial intelligence * Rag * Retrieval augmented generation
