Summary of Agentic Ai-driven Technical Troubleshooting For Enterprise Systems: a Novel Weighted Retrieval-augmented Generation Paradigm, by Rajat Khanda
Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm
by Rajat Khanda
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The novel agentic AI solution presented in this paper tackles the challenge of effective technical troubleshooting in enterprise environments by leveraging a Weighted Retrieval-Augmented Generation (RAG) Framework. This framework dynamically weights retrieval sources such as product manuals, internal knowledge bases, FAQs, and troubleshooting guides based on query context to prioritize the most relevant data. The system employs FAISS for efficient dense vector search and a dynamic aggregation mechanism to seamlessly integrate results from multiple sources. A Llama-based self-evaluator ensures the contextual accuracy and confidence of generated responses before delivery. Preliminary evaluations demonstrate the framework’s efficacy in improving troubleshooting accuracy, reducing resolution times, and adapting to varied technical challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates an AI system that helps companies fix problems by searching through lots of different data sources. It’s like a super-smart librarian who finds the most important information quickly and accurately. The system looks at what kind of problem you’re trying to solve and gives priority to the best places to find answers. It’s really good at finding what you need, which makes it faster and more accurate than other systems. |
Keywords
» Artificial intelligence » Llama » Rag » Retrieval augmented generation
