Lessons on retrieval for autonomous coding agents

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Lessons on retrieval for autonomous coding agents

Hosted by Jason Liu and Nik Pash

Tue, Jul 8, 2025

5:00 PM UTC (1 hour)

Virtual (Zoom)

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Systematically Improving RAG Applications

Jason Liu

View syllabus

Tue, Jul 8, 2025

5:00 PM UTC (1 hour)

Virtual (Zoom)

Free to join

Invite your network

Go deeper with a course

Save 20% til Sunday

Systematically Improving RAG Applications

Jason Liu

View syllabus

What you'll learn

Why RAG distracts coding agents

Learn how retrieval systems create cognitive overhead that reduces agent performance and focus

Metrics-driven RAG evaluation methods

Discover proven frameworks for measuring RAG pipeline effectiveness and ROI impact

When to skip RAG for coding tasks

Identify scenarios where direct code generation outperforms retrieval-augmented approaches

Why this topic matters

RAG has become the default solution for AI applications, but it's often the wrong choice for coding agents. Understanding when NOT to use RAG saves teams months of wasted effort and resources. This lesson teaches you to make strategic architectural decisions that directly impact product success and revenue—skills that distinguish senior engineers from those who follow every trend.

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.