Systematically Improve Your RAG Applications¶

Stop Guessing. Start Building RAG That Actually Works.¶

Top rated AI course on Maven.com (4.8/5 stars, +700 students)

Confidently build and refine Retrieval-Augmented Generation (RAG) systems that deliver real-world impact. Our 6-week, hands-on course takes you from the fundamentals of evaluating quality all the way through building stable, production-grade capabilities.

Enroll now on Maven (starts Nov 17) (20% off for readers)

What People Are Saying¶

"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 and introduced practical techniques that drive continuous product improvements. Grateful for his expertise and support!" — Camu Team (a16z backed)

The Problem With RAG Today¶

Over the last few years, "RAG" has become a buzzword, but making these systems genuinely robust and effective often feels like guesswork. Most teams waste time on:

• ❌ Vague metrics like "make the AI better"
• ❌ Random experiments without data
• ❌ Focusing on generation while ignoring retrieval
• ❌ Building one-size-fits-all systems that underperform

This course cuts through the confusion by giving you a clear, repeatable process: from collecting the right data and generating synthetic evaluations, to gradually incorporating new retrieval indices, routing strategies, fine-tuned embeddings, and practical UX improvements.

What You'll Get¶

In just 6 weeks, you'll learn a proven system to:

• ✅ Build Proper Evaluations - Create synthetic data to measure real improvement
• ✅ Find What Matters - Segment queries to identify high-impact opportunities
• ✅ Improve Search Quality - Build specialized indices that actually retrieve what users need
• ✅ Collect Valuable Feedback - Design UI that generates continuous improvement data
• ✅ Optimize Embeddings - Fine-tune models that understand YOUR definition of relevance

Trusted by Professionals from Leading Organizations:¶

What Makes This Course Different¶

This isn't theory - it's a practical system used by leading companies to:

1. Stop treating RAG as an AI problem
   "RAG is really just a recommendation system squeezed between two LLMs"

2. Focus on what you can control
   Improve search quality first - generation quality follows automatically

3. Build improvement flywheels
   Create systems that get better with every user interaction

No more fumbling in the dark. This program shows you step-by-step how to:

1. Set up meaningful evaluations
2. Identify high-impact opportunities
3. Continuously refine retrieval
4. Integrate feedback loops
5. Enhance product experiences

Enroll now on Maven (starts May 20)

Not ready for a course? Check out my free RAG Playbook¶

Not ready to invest in a paid course yet? Start with my free RAG Playbook newsletter course. You'll get bite-sized lessons delivered straight to your inbox, covering the fundamentals of RAG systems and practical tips for improvement.

Free 6 Day RAG Crash Course

Once you're comfortable with the basics and ready to take your RAG skills to the next level, consider enrolling in our comprehensive course in February 2024.

What You'll Learn¶

Our six-week program is designed to take you from RAG basics to advanced implementation strategies. Perfect for those who deployed RAG systems and want to improve them and cover the last mile of RAG. Here's a breakdown of what you can expect:

Weeks 1-2: Foundations and Evaluation¶

• Synthetic Data Generation: Learn to create high-quality synthetic data for rapid testing and development. Understand the importance of diversity in your test sets and how to avoid common pitfalls.
• Fast Evaluation Techniques: Implement quick, iterative improvements using unit test-like evaluations. Focus on basic retrieval metrics like precision and recall to optimize your system efficiently.
• Query Segmentation: Discover how to categorize and analyze user queries to identify patterns and gaps in your system's performance. Learn to prioritize improvements based on impact, volume, and success likelihood.
• Metrics That Matter: Understand the difference between leading and lagging metrics. Learn how to set actionable goals that drive real improvements in your RAG system.

Weeks 3-4: Advanced Retrieval and Routing¶

• Specialized Indices: Build targeted indices for different content types (documents, images, tables) to improve retrieval accuracy. Learn advanced techniques for handling multimodal data.
• Query Routing: Implement sophisticated query routing systems using parallel function calling. Understand how to select the right tools and APIs for different query types.
• Combining Search Methods: Master the art of blending lexical, semantic, and metadata-based search for optimal results. Learn when and how to use re-rankers effectively.
• Structured Data Extraction: Explore techniques for extracting and leveraging structured data from various sources to enhance your RAG capabilities.

Week 5: Fine-tuning and Embeddings¶

• Embedding Model Optimization: Learn when and how to fine-tune embedding models for your specific use case. Understand the impact of domain-specific data on model performance.
• Data Collection Strategies: Implement effective feedback mechanisms and logging systems to gather valuable data for future improvements.
• Re-ranker Implementation: Discover how to fine-tune and implement re-rankers for better search results. Learn about the latest advancements in ranking technologies.
• Representation Learning: Dive deep into the nuances of creating effective representations for various entities in your system, from user queries to document summaries.

Week 6: Product Design and User Experience¶

• Feedback Collection: Design intuitive and effective feedback mechanisms to continuously improve your system. Learn how to incentivize user feedback without disrupting the experience.
• Streaming Implementations: Implement streaming for improved user experience and perceived performance. Understand the psychological impacts of responsiveness on user satisfaction.
• Advanced Prompting Techniques: Master the art of crafting effective prompts, including chain-of-thought reasoning and dynamic few-shot learning.
• UI/UX Best Practices: Explore cutting-edge UI/UX designs for RAG applications, including innovative ways to display citations, confidence levels, and alternative answers.

Why This Course?¶

In the rapidly evolving field of AI and machine learning, staying ahead means mastering the fundamentals while keeping pace with the latest advancements. Our course offers:

• Practical, Hands-on Learning: Every concept is accompanied by real-world examples and exercises. You'll be implementing and testing ideas from day one.
• Industry-Relevant Case Studies: Learn from actual scenarios encountered in production environments at leading tech companies.
• Expert Instruction: Benefit from 12 hours of dedicated time with instructors who have years of experience in building and optimizing RAG systems.
• Community of Professionals: Connect with a diverse group of peers from companies like Amazon, Adobe, and Zapier. Share insights, challenges, and solutions in a collaborative environment.
• Cutting-edge Content: Stay updated with the latest trends and technologies in RAG, including advanced embedding techniques, multi-modal retrieval, and emerging evaluation metrics.
• Personalized Feedback: Receive tailored advice on your specific RAG challenges through interactive Q&A sessions and project reviews.

More From Our Students¶

"Jason's RAG Flywheel methodology transformed how our team approaches retrieval systems - instead of endless prompt tweaking and "vibe checks," we now have concrete evals that pinpoint exactly where failures occur. The structured approach to fine-tuning embeddings and query segmentation gave us immediate 30% accuracy gains on domain-specific queries." — chiheb dkhil, Founder and CTO, Cynergis AI

"If you've already shipped a RAG prototype and want a repeatable path to reliability, this course delivers a practical, evaluation‑first playbook. It focuses on diagnosing failure modes, strengthening retrieval, and closing the loop with data and metrics - rather than building yet another demo. You'll get the most from it if you've deployed a RAG system, but the structured format, office hours, and hands‑on notebooks make it a strong choice for teams moving from vibe checks to measurable improvements." — Hendrik Reh, Indie Consultant, Blacksmith Consulting

"A highly valuable course for anyone serious about building and scaling RAG applications. It combines theory, hands-on frameworks, and a strong community. I highly recommend it to anyone ready to move beyond experimentation and deliver production-ready RAG systems." — Thomas Bärtschi, Product Leader, GN Group

"Jason did a wonderful job on making clear that the hard work is never gone, which is certainly true for building RAG applications. Data, though partially synthetic, and systematic evaluations are still the core driver for the quality of retrieval and subsequent generation. It's a good mix of lectures about the methodology, hands-on notebooks and guest lectures highlighting various focus points." — Jan Selis, Senior Consultant, Synergy2core

"Essential course for AI practitioners. Jason delivers a systematic approach to RAG systems that perfectly balances theory with practical implementation. The well-structured notebooks provide an invaluable framework for experimentation. My key takeaway was the experimentation mindset cultivated throughout - critical for navigating the rapidly evolving AI landscape. The guest lectures and community of hands-on practitioners bring diverse perspectives and insights that enrich the learning experience." — Saumil Srivastava, Individual Consultant, Self Employed

"I took Jason's RAG course in Cohort 3 and found it to be an information-dense, practically useful LLM course. It skips the hype and focuses on real tools and patterns you can apply. Jason's cross-industry experience brings valuable perspective on how retrieval systems are being used in production to drive real outcomes. Solid course if you're looking to learn how to build a RAG system with a systems mindset - something that evolves over time rather than a one-off implementation." — Nishant Hegde, Staff Analytics Engineer, Netflix

"Incredible Course! I learned a tremendous amount about improving my RAG app through synthetic data, re-rankers, training embedding models and more. Most importantly, it improved my data intuition and how to approach evals. Strongly recommended." — Philip Park, Data Scientist, Phi Ventures USA

"This course is a must-have. The opportunity to learn from experienced practitioners who have navigated the challenges of bringing a product or service to market is incredibly valuable. This of it as ML of LLMs. What sets it apart is Jason's commitment to building a community of hands-on practitioners who exchange the latest developments, which is priceless in such a fast-paced industry." — Paul Perry, CTO, Ampliforce

"This course is very comprehensive and provides practical tools to help understand how to improve RAG systems. I like that the course has both theory + application (via jupyter notebook code samples) + industry (via guest lectures). Highly recommend for anyone building enterprise RAG systems!" — Claudia Ng, ML Engineer

"As a Data Scientist for the past 10 years, this course opened up so many questions and issues I have had with understanding and developing RAG applications. It makes you go from subjective measures to objective measures for improving your RAG applications. This course is a must whether you are just beginning developing RAG applications (like me) or have been doing so for a while." — Leo Carlsson, Data Scientist, SSAB

"Excellent and comprehensive course that has evolved my approach to LLM Ops as a whole. Jason does a great job balancing technical and executive learning. It's very clear that he's been in the game for a long time at a high level." — Uzair Qarni, CTO, Gepeto

"This course has given me a lot of tools and tips to take my RAG systems to the next level! We were exposed to not only Jason's wealth of knowledge, but also a bunch of guest talks that had their own specialties. I really appreciate the focus on actually having a systematic way of building good RAG apps. I now have the tools to build some pretty awesome apps! I highly recommend this course if you're serious about deploying RAG apps in production!" — Kameran Kolahi, Senior Data Scientist, Product Analytics, BOLD

"This course's content marked a significant turning point in my approach. It dramatically influenced my workflow for building generative AI systems, not just RAGs. Jason and Ivan did an amazing job condensing so much valuable content into just 6 weeks. I highly recommend this course to anyone working on generative AI systems who wants to take evaluation seriously." — Arian Pasquali, ML Engineer, Faktion AI

"I came into this course with zero clue how to build a production-grade RAG pipeline (my prototypes were, erm, mostly very basic). Now? I've got a concrete, step-by-step playbook. What stood out for me: • The "Improvement Flywheel" approach—finally, a way to move past endless guesswork and actually measure what's working (and what's not). • Real examples and frameworks for fine-tuning embeddings, collecting user feedback, and segmenting queries. No hand-waving—just actionable tactics." — Gang Rui, Product Builder, Entrepreneur

"Great course! I learned a lot. Jason brings a wealth of experience, and his approach to building RAG systems strikes a perfect balance between strong fundamentals and practical application." — Michał Prządka, Consultant, self employed, independent

"When dealing with LLMs and RAGs, there's a sea of information out there. What I found most valuable about the course is that it acts as a compass, helping you sail through this sea in the right direction. There's still a lot to learn, but at least now you know where you're heading — and you're less likely to get distracted by the shiny new tools that seem to appear every day in this field." — Ivan Castano, Consultant, Unconventional Wisdom

"I would put this course on par with a Master's degree at a leading institution. The price was one of the main factors that held me back as someone paying for it myself. But as we come to a close, I'm glad I got in before any price increases. The community is underrated too. Access to some of the top minds in the industry while it's so small can't be understated." — Johnny Heo, Founder, Omni

"Outstanding class to fully understanding common problems with RAG. The class teaches systemic and repeatable processes for measuring RAG accuracy and how to improve RAG. Many concepts are provided which are very useful." — Dan Sullivan, Systems Engineer, NightWing

Risk-Free Guarantee¶

We're so confident in the value of this course that we offer a money-back guarantee. If you don't feel you're making significant progress in improving your RAG applications after 4 weeks, we'll refund your course fee, no questions asked.

Secure Your Team's Spot Today¶

The field of RAG is evolving quickly. Don't fall behind.

Enroll now on Maven (starts Nov 17)