Systematically Improving RAG Applications

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.

DOWNLOAD LINK: Systematically Improving RAG Applications

Systematically_Improving_RAG_Applications.part1.rar – 3.0 GB
Systematically_Improving_RAG_Applications.part2.rar – 3.0 GB
Systematically_Improving_RAG_Applications.part3.rar – 3.0 GB
Systematically_Improving_RAG_Applications.part4.rar – 3.0 GB
Systematically_Improving_RAG_Applications.part5.rar – 1.7 GB

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