Transform your retrieval from “good enough” to “mission-critical” in weeks, not months

Most RAG implementations get stuck in prototype purgatory. They work well for simple cases but fail on complex queries—leading to frustrated users, lost trust, and wasted engineering time. The difference between a prototype and a production-ready system isn’t just better technology, it’s a fundamentally different mindset.

The RAG Implementation Reality

What you’re experiencing right now:

What your RAG system could be:

With the RAG Flywheel methodology, you’ll build a system that:

What Makes This Course Different

Unlike courses that focus solely on technical implementation, this program gives you the systematic, data-driven approach used by companies to transform prototypes into production systems that deliver real business value:

The Complete RAG Implementation Framework

Week 1: Evaluation Systems

Build synthetic datasets that pinpoint RAG failures instead of relying on subjective assessments

BEFORE: “We need to make the AI better, but we don’t know where to start.”

AFTER: “We know exactly which query types are failing and by how much.”

Week 2: Fine-tune Embeddings

Customize models for 20-40% accuracy gains with minimal examples

BEFORE: “Generic embeddings don’t understand our domain terminology.”

AFTER: “Our embedding models understand exactly what ‘similar’ means in our business context.”

Week 3: Feedback Systems

Design interfaces that collect 5x more feedback without annoying users

BEFORE: “Users get frustrated waiting for responses and rarely tell us what’s wrong.”

AFTER: “Every interaction provides signals that strengthen our system.”

Week 4: Query Segmentation

Identify high-impact improvements and prioritize engineering resources

BEFORE: “We don’t know which features would deliver the most value.”

AFTER: “We have a clear roadmap based on actual usage patterns and economic impact.”

Week 5: Specialized Search

Build specialized indices for different content types that improve retrieval

BEFORE: “Our system struggles with anything beyond basic text documents.”

AFTER: “We can retrieve information from tables, images, and complex documents with high precision.”

Week 6: Query Routing

Implement intelligent routing that selects optimal retrievers automatically

BEFORE: “Different content requires different interfaces, creating a fragmented experience.”

AFTER: “Users have a seamless experience while the system intelligently routes to specialized components.”

Real-world Impact From Implementation

Join 400+ engineers who’ve transformed their RAG systems with this methodology

Your Instructor

Jason Liu has built AI systems across diverse domains—from computer vision at the University of Waterloo to content policy at Facebook to recommendation systems at Stitch Fix that boosted revenue by $50 million. His background in managing large-scale data curation, designing multimodal retrieval models, and processing hundreds of millions of recommendations weekly has directly informed his consulting work with companies implementing RAG systems.