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.
