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## Data-Driven Product Development for AI Applications
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## A Systematic Approach to Building Self-Improving AI Products
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_The systematic approach that helped companies achieve 5x feedback rates, 87% retrieval accuracy, and $50M+ revenue improvements_
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!!! success "Proven Track Record"
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⭐ **Top rated AI course (4.7/5 stars, +200 students)**
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Trusted by professionals from OpenAI, Anthropic, Google, Microsoft, and 50+ leading organizations
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!!! warning "The RAG Reality Check"
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**90% of RAG implementations fail** because teams focus on model selection and prompt engineering while ignoring the fundamentals: measurement, feedback, and systematic improvement.
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This book changes that. Based on real-world experience with companies like HubSpot, Zapier, and many others, you'll learn the exact frameworks that transform RAG from a disappointing demo into a revenue-generating product.
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# Trusted by Professionals from Leading Organizations:
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These are the companies that took our masterclass.
|[Redhat](https://redhat.com)| Software Development |
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|[Nvidia](https://nvidia.com)| AI |
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</div>
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## What Students Are Saying
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!!! quote "Real Impact from Real Students"
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|**Review**|**Name & Role**|
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|------------|-----------------|
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|*"Practical lessons from every lecture... learning from a community on the vanguard of this emerging field."*|**Max**, Software Engineer, Launch School |
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|*"Jason helped us break down our vision into actionable steps, providing clear recommendations on the best models for each use case. His guidance gave us a tangible roadmap for our next steps."*|**Camu Team** (a16z backed) |
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|*"Excellent job of stressing the fundamentals... useful metric tools to measure and improve RAG systems."*|**Christopher**, Senior Data/AI Architect, Procurement Sciences AI |
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|*"Jason and Dan help set you on the right path... emphasis on looking at your data and building a metrics-based flywheel."*|**Vitor**, Staff Software Engineer, Zapier |
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|*"Practical and grounded in actual industry experience... like getting the inside scoop from folks who've been in the trenches."*|**Ashutosh**, Senior Principal Scientist, Adobe |
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|*"System-oriented approach... Highly relevant, directly applicable, and save time in building prototypes."*|**Mani**, Senior Principal Software Engineer, Red Hat |
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## The Problem: Why Most RAG Systems Fail
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!!! quote "Real Patterns from the Field"
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After working with dozens of companies, the failure pattern is predictable:
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**Week 1-2:** "Our RAG demo is amazing!"
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**Week 3-4:** "Why are users getting irrelevant results?"
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**Week 5-6:** "Let's try a different model..."
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**Week 7-8:** "Maybe we need better prompts..."
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**Week 9+:** "Our users have stopped using it."
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Sound familiar? You're not alone. The issue isn't your technology—it's your approach.
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```
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This isn't just theory. Companies using this approach have achieved:
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-**5x increase** in feedback collection (changing one line of copy!)
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-**87% retrieval accuracy** (up from 63% baseline)
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-**45% reduction** in perceived latency
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-**$50M+ revenue impact** through improved recommendations
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!!! success "The Flywheel Mindset"
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Your RAG application should be smarter next month than it is today. If it isn't, something is wrong with your process, not your technology.
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## What You'll Build: A Proven 6-Chapter Journey
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### [Introduction: The Product Mindset Shift](workshops/chapter0.md)
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**The Foundation That Changes Everything**
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Stop thinking like an engineer. Start thinking like a product leader. Learn why treating RAG as a product rather than a project is the #1 predictor of success.
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**Key concepts:** The improvement flywheel • Common failure patterns • Product thinking vs implementation thinking
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_A systematic approach to building self-improving AI systems_
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---
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!!! abstract "About This Book"
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This book provides a structured approach to evolving Retrieval-Augmented Generation (RAG) from a technical implementation into a continuously improving product. You'll learn to combine product thinking with data science principles to create AI systems that deliver increasing value over time.
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### [Chapter 1: Starting the Data Flywheel](workshops/chapter1.md)
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**From Zero to Evaluation in Days, Not Months**
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## The RAG Improvement Flywheel
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The cold-start problem kills most RAG projects. Learn the synthetic data techniques that get you from zero to measurable improvement in days.
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At the core of this book is the RAG improvement flywheel - a continuous cycle that transforms user interactions into product enhancements.
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**You'll build:** Synthetic evaluation datasets • Precision/recall frameworks • Leading vs lagging metrics • Experiment velocity tracking
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!!! tip "Beyond Technical Implementation"
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This book goes beyond teaching you how to implement RAG. It shows you how to think about RAG as a product that continuously evolves to meet user needs and deliver business value.
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**Case study:** Legal tech company improved retrieval from 63% to 87% in 2 weeks using these techniques
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## Chapters
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---
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### [Introduction: Beyond Implementation to Improvement](workshops/chapter0.md)
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### [Chapter 2: From Evaluation to Enhancement](workshops/chapter2.md)
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**Fine-Tuning That Actually Moves Business Metrics**
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Understand why systematic improvement matters and how to approach RAG as a product rather than just a technical implementation.
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Stop guessing which model to use. Learn how to systematically improve retrieval through fine-tuning, re-ranking, and targeted enhancements.
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### [Chapter 1: Starting the Flywheel](workshops/chapter1.md)
**Case study:** Enterprise system handling millions of queries with 95%+ routing accuracy
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---
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Create a cohesive product experience that intelligently routes to specialized components while maintaining a seamless user experience.
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### [Conclusion: Product Principles for AI Applications](misc/what-i-want-you-to-takeaway.md)
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**The Lessons That Survive Every Technology Shift**
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### [Key Takeaways: Product Principles for AI Applications](misc/what-i-want-you-to-takeaway.md)
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Models change. Principles endure. Take away the core insights that will guide your AI product development for years to come.
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Core principles that will guide your approach to building AI products regardless of how the technology evolves.
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## Learn from Industry Leaders: 20+ Expert Talks
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## Talks and Presentations
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!!! info "Featured Lightning Lessons"
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Companies like Zapier, ChromaDB, LanceDB, Glean, and Sourcegraph share their battle-tested strategies
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Explore insights from industry experts and practitioners through our collection of talks, lightning lessons, and presentations:
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### Featured Talks
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### [Featured Talks](talks/index.md)
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**[How Zapier 4x'd Their AI Feedback](talks/zapier-vitor-evals.md)** - Vitor (Staff Engineer, Zapier) reveals the one-line change that transformed their feedback collection
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-**[Fine-tuning Re-rankers and Embedding Models for Better RAG Performance](talks/fine-tuning-rerankers-embeddings-ayush-lancedb.md)** - Practical approaches to enhancing retrieval quality (Ayush from LanceDB)
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-**[RAG Anti-patterns in the Wild](talks/rag-antipatterns-skylar-payne.md)** - Common mistakes and how to fix them (Skylar Payne)
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-**[Semantic Search Over the Web with Exa](talks/semantic-search-exa-will-bryk.md)** - Building AI-first search engines (Will Bryk)
-**[Online Evals and Production Monitoring](talks/online-evals-production-monitoring-ben-sidhant.md)** - Monitoring AI systems at scale (Ben Hylak & Sidhant Bendre)
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*"Jason helped us set you on the right path... emphasis on looking at your data and building a metrics-based flywheel."* - **Vitor**, Staff Software Engineer, Zapier
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[View all talks →](talks/index.md)
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**[The 12% RAG Boost You're Missing](talks/fine-tuning-rerankers-embeddings-ayush-lancedb.md)** - Ayush (LanceDB) shows why re-rankers are the "low-hanging fruit" everyone ignores
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**[Why Cline Ditched RAG Entirely](talks/rag-is-dead-cline-nik.md)** - Nik Pash explains why leading coding agents abandoned embeddings for direct exploration
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**[The RAG Mistakes Killing Your AI](talks/rag-antipatterns-skylar-payne.md)** - Skylar Payne exposes the anti-patterns that 90% of teams fall into
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**[Stop Trusting MTEB Rankings](talks/embedding-performance-generative-evals-kelly-hong.md)** - Kelly Hong reveals why public benchmarks fail in production
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[Explore all 20+ talks with actionable insights →](talks/index.md)
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## For Product Leaders, Engineers, and Data Scientists
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Begin your journey by reading the [Introduction](workshops/chapter0.md) or jump directly to [Chapter 1](workshops/chapter1.md) to start building your evaluation framework and data foundation.
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## Risk-Free Learning
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!!! tip "100% Satisfaction Guarantee"
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We're so confident in the value of this approach that we offer a **money-back guarantee**. If you don't see significant improvements in your RAG system's performance after following our methodology for 4 weeks, we'll refund your investment, no questions asked.
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Your success is our success. We've seen this framework work for companies from startups to Fortune 500 enterprises.
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## Stay Updated
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Get access to exclusive discounts and our free 6-day email course on RAG improvement:
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