Improving retrievers by Reranking and embedding fine-tuning

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Improving retrievers by Reranking and embedding fine-tuning

Hosted by Jason Liu and Ayush Chaurasia

Wed, Jul 2, 2025

5:00 PM UTC (1 hour)

Virtual (Zoom)

Free to join

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117 students

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Systematically Improving RAG Applications

Jason Liu

View syllabus

Wed, Jul 2, 2025

5:00 PM UTC (1 hour)

Virtual (Zoom)

Free to join

117 students

Invite your network

Go deeper with a course

Systematically Improving RAG Applications

Jason Liu

View syllabus

What you'll learn

Reranking Fundamentals

Master reranker architecture and implementation to effectively boost retrieval accuracy beyond embedding models alone.

Strategic Model Fine-tuning

Apply practical criteria for deciding when to fine-tune embeddings vs. implementing rerankers based on use case demands.

Performance Optimization

Evaluate deployment tradeoffs to select optimal reranking and embedding approaches for various hardware environments.

Why this topic matters

Mastering reranking and embedding fine-tuning helps you build retrieval systems that deliver genuinely relevant results, not just basic search. These skills let you optimize for accuracy, speed, and cost—making you valuable for developing production-ready AI applications that outperform competitors.

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.