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:

• Your RAG demo impressed stakeholders, but real users encounter hallucinations when they need accuracy most
• Engineers spend countless hours tweaking prompts with minimal improvement
• Colleagues report finding information manually that your system failed to retrieve
• You keep making changes but have no way to measure if they’re actually helping
• Every improvement feels like guesswork instead of systematic progress
• You’re unsure which 10% of possible enhancements will deliver 90% of the value

What your RAG system could be:

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

• Retrieves the right information even for complex, ambiguous queries
• Continuously improves with each user interaction
• Provides clear metrics to demonstrate ROI to stakeholders
• Allows your team to make data-driven decisions about improvements
• Adapts to different content types with specialized capabilities
• Creates value that compounds over time instead of degrading

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 Improvement Flywheel: Build synthetic evaluation data that identifies exactly what’s failing in your system—even before you have users
• Fine-tuning Framework: Create custom embedding models with minimal data (as few as 6,000 examples)
• Feedback Acceleration: Design interfaces that collect 5x more high-quality feedback without annoying users
• Segmentation System: Analyze user queries to identify which segments need specialized retrievers for 20-40% accuracy gains
• Multimodal Architecture: Implement specialized indices for different content types (documents, images, tables)
• Query Routing: Create a unified system that intelligently selects the right retriever for each query

What You’ll Learn In Systematically Improving RAG Applications

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

• 85% blueprint image recall: Construction company using visual LLM captioning
• 90% research report retrieval: Through better text preprocessing techniques
• $50M revenue increase: Retail company enhancing product search with embedding fine-tuning
• +14% accuracy boost: Fine-tuning cross-encoders with minimal examples
• +20% response accuracy: Using re-ranking techniques
• -30% irrelevant documents: Through improved query segmentation

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

Sale Page: Maven – Systematically Improving RAG Applications