Episode 27: Enhancing RAG based Gen AI Applications with Unstructured Data

Episode 27: Enhancing RAG based Gen AI Applications with Unstructured Data

Today we join Maria Khalusova, Staff Developer Advocate with Unstructured.IO, to discuss how companies can unlock their Unstructured Data to deliver better results from their Large Language Models. We talk about how Unstructured Data can enhance the performance of RAG applications, RAG vs Fine Tuning, data Chunking, Multi-Modal models and more.

AWS Hosts: Nolan Chen & Malini Chatterjee

Unstructured Enterprise Platform beta signup:

https://unstructured.io/platform


Embedding models MTEB Leaderboard:

https://huggingface.co/spaces/mteb/leaderboard


2019 Deloitte report (source of the statistics that only 18% of organizations were using unstructured data):

https://www2.deloitte.com/us/en/insights/topics/analytics/insight-driven-organization.html


80% of data is unstructured, source: https://mitsloan.mit.edu/ideas-made-to-matter/tapping-power-unstructured-data


Papers showing RAG outperforming fine-tuning:

https://arxiv.org/abs/2312.05934

https://arxiv.org/abs/2401.08406


Email Your Feedback: rethinkpodcast@amazon.com

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Jaksot(55)

Episode 55: When Agents Break: Building a Recovery Layer for Agentic AI with Cohesity

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Episode 54: Autonomous Supply Chains Are Here - FourKites on Agentic AI in Action

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Episode 53: AWS re:Think - AWS Summits Recap Part 2

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Episode 52: AWS re:Think - AWS Summits Recap Part 1

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Episode 51: Rethinking Cloud Security in the Age of Zero-Days and AI

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Episode 50: Rethink Agentic AI: From Shadow Risk to Strategic Value

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Episode 49: Rethinking AI Agent Evaluations

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