Maven - Systematically Improving RAG Applications [Update 08/2025]
English | Size: 16.8 GB
Genre: eLearning[/center]
Staff machine learning engineer, currently working as an AI consultant
Stop building RAG systems that impress in demos but disappoint in production
Transform your retrieval from "good enough" to "mission-critical" in weeks, not months. Most RAG systems stall in prototype purgatory: they demo well, but fail on complex queries-eroding trust and wasting engineering time. The difference isn't just better tech, but a systematic mindset.
With the RAG Flywheel, you'll:
✅ Pinpoint failures with synthetic evals
✅ Fine-tune embeddings for 20-40% gains
✅ Collect 5x more user feedback
✅ Segment queries to target high-impact fixes
✅ Build multimodal indices for docs, tables, images
✅ Route queries to the best retriever automatically
Week by week, you move from vague "make it better" to clear metrics, focused improvements, and compounding value. Real-world results include +20% accuracy from re-ranking, +14% with cross-encoders, and $50M revenue boosts from better search.
Join 400+ engineers applying this framework in production. Instructor Jason Liu has built multimodal retrieval and recommendation systems at Facebook, Stitch Fix, and through consulting-experience that shaped this practical, battle-tested approach.
What you'll learn
Follow a repeatable process to continually evaluate and improve your RAG application
Analyze and Diagnose RAG System Performance
Evaluate retrieval quality using precision, recall, and MRR metrics to identify system weaknesses
Differentiate between leading metrics (experiments run) and lagging metrics (customer satisfaction) to drive actionable improvements
Design synthetic data generation pipelines that enable rapid experimentation without waiting for user data
Construct Data-Driven Improvement Frameworks
Create comprehensive evaluation datasets using LLMs to generate realistic query-answer pairs
Establish baselines using tools like LanceDB to benchmark different retrieval implementations
Design Specialized Search Systems
Develop multimodal retrieval systems that handle documents, images, tables, and structured data
Synthesize lexical (BM25), semantic (embeddings), and metadata-based search for optimal results
Optimize Query Understanding & Routing
Extract structured information from diverse data sources to enable precise filtering
Classify queries using domain expertise and few-shot classifiers to improve routing accuracy
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