Systematically Improving RAG Applications Download

Systematically Improving RAG Applications

Turn Prototype RAG Systems into Reliable, Production-Grade Solutions

This practical course teaches you a proven, data-driven methodology to elevate Retrieval-Augmented Generation (RAG) applications from demo-level performance to mission-critical reliability. Instead of endless trial-and-error tweaks, you’ll learn systematic ways to identify weaknesses, measure progress, and deliver consistent accuracy—even on complex, real-world queries.

✅ The Common RAG Struggle vs. Production Reality

Many teams build impressive prototypes that shine in simple cases but falter in production: hallucinations on important questions, manual searches by frustrated users, wasted engineering time, and unclear improvement metrics. This program shifts you to a system that reliably retrieves the right information, improves continuously with use, provides measurable ROI, and compounds value over time.

✅ Unique Data-Driven Approach (The RAG Flywheel)

Unlike purely technical courses, this methodology—used by leading companies—focuses on systematic progress:

• Synthetic evaluation data to spot failures early
• Custom embedding fine-tuning with minimal examples
• Smart feedback collection (5x more signals without frustrating users)
• Query segmentation for targeted 20–40% accuracy gains
• Multimodal support for documents, images, and tables
• Intelligent query routing for seamless performance

✅ Week 1: Building Strong Evaluation Systems

Create synthetic datasets to precisely identify where your RAG fails—no more guessing or subjective judgments. Result: Clear insight into failing query types and exact performance gaps.

✅ Week 2: Fine-Tuning Embeddings

Customize embedding models for your specific domain, achieving 20–40% accuracy improvements even with as few as 6,000 examples. Result: Embeddings that truly understand your business terminology and context.

✅ Week 3: Powerful Feedback Systems

Design user interfaces that naturally gather high-quality feedback during normal interactions. Result: Every user session strengthens the system without added friction.

✅ Week 4: Query Segmentation and Prioritization

Analyze real usage patterns to discover high-impact segments and build a prioritized roadmap based on business value. Result: Focused engineering efforts that deliver maximum results quickly.

✅ Week 5: Specialized Search Capabilities

Develop dedicated indices for different content types (text, tables, images) to dramatically improve retrieval precision. Result: Accurate handling of complex documents beyond basic text.

✅ Week 6: Intelligent Query Routing

Create a unified system that automatically selects the best retriever for each query. Result: A smooth user experience backed by optimized, specialized components.

✅ Proven Real-World Results

Participants and case studies have achieved:

• 85% recall on blueprint images (via visual captioning)
• 90% retrieval of research reports (better preprocessing)
• $50M revenue uplift (improved product search)
• +14% accuracy from cross-encoder fine-tuning
• +20% response accuracy with re-ranking
• 30% reduction in irrelevant results via segmentation

✅ Taught by Industry Expert Jason Liu

Jason brings extensive experience from computer vision research, content systems at Facebook, and recommendation engines at Stitch Fix (driving $50M in revenue). His work in large-scale data curation and multimodal retrieval directly shapes this practical, production-focused training.

✅ Join Hundreds of Successful Engineers

Over 400 engineers have already used this methodology to transform their RAG systems into reliable, high-value tools that earn stakeholder trust and deliver measurable business impact.

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