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5-Point RAG Strategy Guide to Prevent Hallucinations & Bad Answers!

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[ Key Takeaways ]

This guide designed to help teams working on GenAI Initiatives gives you five actionable strategies for RAG pipelines that will improve answer quality and prevent hallucinations.

[ Overview ]

About the Guide

This guide written by Sedarius Tekara Perrotta and the data science team at Shelf is based on 10 years of experience processing over a 100 million sections of unstructured data for some of the world’s best brands, including; Amazon, Lufthansa and Nestle.

According to a poll of IT leaders by Gartner, poor data quality is the #1 obstacle companies have in GenAI initiatives.

Poor Data Quality Matters

Poor data quality and AI is nothing new. What has changed is the issue is now unstructured data quality. In fact, 32% of enterprise unstructured data is inaccurate and is being fed right into LLMs.

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[ Quotes ]

What our customers say

“The Shelf solution is superb! We achieved a 25% reduction in average handling time in the first three months of going live with Shelf.”

“Overall, Shelf has been fantastic and the integration into our CCaaS environment was quick and easy. Some of my favorite functionalities are Answer Assist, the ability to integrate with a chatbot, search functionality inside of documents, and the multi-language capabilities.”

“Shelf has provided us with a solution to our knowledge needs. We now have a single source of truth that our advisors can look to when helping customers. Shelf is easy to use for both advisors and administrators, and we’ve seen improvements in a number of metrics since implementation.”

“I have been using Shelf for a long time and I am extremely impressed with its knowledge management and content organization capabilities. The product greatly reduces time and improves efficiency because there is no confusion about outdated or incorrect material.”

“Shelf is even better than expected, and it’s great to be surprised like that. Usually it’s the opposite. Search Copilot reduced handle time on our email queue by 80%!”

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[ Library ]

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