Better RAG Through Better Data

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Building AI-Native Products

Better RAG Through Better Data

Hosted by Jason Liu and Adit Abraham

Wed, Jun 18, 2025

6:00 PM UTC (1 hour)

Virtual (Zoom)

Free to join

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100 students

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Systematically Improving RAG Applications

Jason Liu

View syllabus

Wed, Jun 18, 2025

6:00 PM UTC (1 hour)

Virtual (Zoom)

Free to join

100 students

Invite your network

Go deeper with a course

Systematically Improving RAG Applications

Jason Liu

View syllabus

What you'll learn

Parsing Precision

Learn precise document parsing techniques to extract high-quality data for accurate retrieval systems.

Optimal Data Representation

Apply specialized formatting strategies that maximize performance in both language and embedding models.

Enterprise-Scale RAG Solutions

Implement advanced ingestion methodologies that maintain quality while handling large-scale document collections.

Why this topic matters

Understanding RAG data optimization is crucial as AI adoption grows. By mastering better parsing, data representation, and ingestion techniques, you'll build more accurate AI systems that deliver reliable answers. These skills distinguish average from excellent RAG implementations, making you a valuable asset in the evolving AI engineering landscape.

You'll learn from

Jason Liu

Consultant at the intersection of Information Retrieval and AI

Jason has built search and recommendation systems for the past 6 years. He has consulted and advised a dozens startups in the last year to improve their RAG systems. He is the creator of the Instructor Python library.

Adit Abraham

Co-founder & CEO of Reducto

Adit is the co-founder and CEO of Reducto, a company that helps leading AI teams from startups through Fortune 10 parse and extract data from complex documents. Prior to Reducto, Adit studied CS at MIT and worked in ML research and product roles at MIT's Media Lab, BlinkAI, and Google.

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