4.7 (29)
4 Weeks
·Cohort-based Course
Follow a repeatable process to continually evaluate and improve your RAG application
4.7 (29)
4 Weeks
·Cohort-based Course
Follow a repeatable process to continually evaluate and improve your RAG application
Instructor Clients
Course overview
Master RAG Development & Transform Your AI Products
Acquire the skills to confidently build, improve, and iterate on RAG applications
Become the RAG Product Leader Your Company Needs
Companies are racing to integrate AI features that truly deliver value. To stay ahead, you must master the art of building Retrieval-Augmented Generation (RAG) products that continuously improve—no luck or guesswork required.
4 Weeks to Transform Your AI Product Sense
This course removes the mystery and turns RAG from a risky experiment into a structured, data-driven practice. You'll learn how to pinpoint what's working, diagnose what's not, and steadily raise the bar on performance and user satisfaction.
Course Format
Our program follows a "flipped classroom" approach where you access all core lessons as pre-recorded videos to watch on your own schedule. This flexible format lets you learn at your own pace while still getting personalized support.
Throughout each week, we offer multiple Office Hours sessions at various days and times. These optional sessions provide valuable opportunities to:
- Ask questions about course material
- Get direct feedback from Jason
- Connect with fellow participants
We recommend attending at least one Office Hour session per week. With multiple Office Hour sessions available, you'll have plenty of chances to get your specific questions answered directly by Jason in a small group environment.
The curriculum is further enriched by special guest lectures from industry experts who bring additional perspectives and insights to complement the core material.
What You'll Learn
Master RAG Fundamentals — Build repeatable evaluation systems with synthetic data that drive measurable improvements.
Decode User Behavior — Transform complex query patterns into actionable insights using powerful dashboards that prioritize high-impact changes.
Conquer Multimodal Content — Extract precision metadata and create specialized indexes that handle diverse content formats without quality loss.
Perfect Query Routing — Blend multiple search methods into a seamless system that intelligently directs queries for guaranteed results.
Optimize Representations — Deploy domain-specific vectors and re-rankers that dramatically outperform generic embeddings with minimal data.
Design Feedback-Rich UX — Create interfaces that eliminate perceived latency while collecting invaluable user signals for continuous improvement.
Why This Matters
RAG has become a core differentiator for AI-driven products. The strategies you learn here will never go out of style. As tools and models evolve, you'll know how to integrate them, measure their impact, and refine your approach—over and over.
What's Included
- Proven evaluation frameworks that demystify "is it working?"
- Techniques to handle more data types, faster and more accurately
- Methods for continuous feedback collection and refinement
- Insights that adapt as the AI landscape changes
About Your Instructor
Jason Liu is a seasoned AI consultant who has guided AI engineering at Stitch Fix and high-growth startups from Seed stage to public companies.
He also advises many of today's generative AI startups. Jason specializes in transforming vague AI ambitions into concrete, measurable practices that deliver real business value.
Who Should Apply
- Product teams integrating AI features into real software products
- AI engineers moving from prototype to production-grade performance
- Anyone committed to developing true "AI product sense"—not just hype
We're so confident in the course's value that if you don't see meaningful improvements in your product's performance within four weeks of starting, we'll refund you fully.
Don't let uncertainty stall your AI progress.
Join a small cohort of dedicated practitioners, learn the proven frameworks, and build the RAG systems your users will love.
Getting started is easy.
1️⃣ Begin with my free 6-day RAG Playbook email course. You'll get bite-sized lessons delivered straight to your inbox, covering the fundamentals of RAG systems and practical tips for improvement at improvingrag.com
2️⃣ Join the 4-week cohort to master RAG basics and advanced implementation strategies. Perfect for those who deployed RAG systems and want to improve them and cover the last mile of RAG.
🔥 All alumni can stay up to date with our private community that includes personalized support, peer networking, and more!
📣 Live Office Hour sessions are at 9AM EST, 1PM EST and 2PM EST! You can pick whichever works best for you! All live sessions are recorded and the main content is delivered asynchronously so you can watch it on demand whenever you want.
Interested in an enterprise deal so your whole team or company can take the course? Please reach out directly to support@jxnl.co!
01
A product leader, engineer, or data scientist looking to move beyond ad-hoc RAG prototypes into scalable, production-grade AI solutions.
02
A professional who understands LLM basics but wants a repeatable, data-driven methodology to improve retrieval relevance, latency, and user
03
Eager to create feedback loops that continuously refine and enhance the quality of RAG applications as models, data, and user needs evolve.
Adopt a Systematic, Data-First Methodology
Implement the Data and Evals Flywheel approach to continuously develop and improve RAG applications—breaking free from guesswork and relying on measurable, iterative enhancements.
Run Fast, Unit-Test-Like Evaluations
Quickly assess your retrieval systems using precision and recall metrics, identify bottlenecks, and confidently validate changes without sinking into endless trial-and-error cycles.
Leverage Synthetic Data for Rapid Iteration
Generate and utilize synthetic data sets to speed up experimentation, enabling you to test new approaches, embeddings, and architectures before committing full resources.
Master Fine-Tuning & Hard Negative Mining
Apply fine-tuning strategies for embedding models to boost search relevance and explore hard negative examples to further sharpen retrieval performance.
Classify Queries & Identify Bottlenecks
Use query classification and segmentation techniques to pinpoint exactly where your RAG system falls short—whether it’s due to limited inventory or insufficient capabilities.
Design Specialized Indices for Multiple Modalities
Create tailored indices for documents, images, tables, SQL databases, and more. Learn when and how to fuse or layer these indices to handle complex retrieval tasks elegantly.
Enhance Retrieval with Summarization & Chunking
Implement synthetic text chunk generation and strategic summarization methods to improve retrieval results, ensuring end-users get clear, concise, and contextually rich answers.
Implement Query Routing & Index Fusion
Develop systems that intelligently route queries to the right indices, tools, or pipelines. Blend and fuse indices effectively to handle nuanced, multi-step queries.
Optimize Both Global & Local Performance
Evaluate the performance of your routing logic and each individual index separately. Gain the nuance to fine-tune global system performance and local retrieval accuracy in tandem.
Integrate Feedback Loops for Continuous Improvement
Design explicit and implicit feedback mechanisms—capturing user signals, automating re-labeling, and applying improvements in real-time to keep your RAG systems on an upward trajectory.
18 interactive live sessions
Lifetime access to course materials
25 in-depth lessons
Direct access to instructor
Projects to apply learnings
Guided feedback & reflection
Private community of peers
Course certificate upon completion
Maven Satisfaction Guarantee
This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.
Systematically Improving RAG Applications
May
20
Introductions
May
21
Guest Lecture [Coming Soon]
May
20
Office Hour
May
20
Office Hour
May
22
Office Hour
May
28
Guest Lecture [Coming Soon]
May
27
Office Hour
May
27
Office Hour
May
29
Office Hour
Jun
4
Guest Lecture [Coming Soon]
Jun
3
Office Hour
Jun
3
Office Hour
Jun
5
Office Hour
Jun
11
Guest Lecture [Coming Soon]
Jun
12
Conclusions
Jun
10
Office Hour
Jun
10
Office Hour
Jun
12
Office Hour
4.7 (29 ratings)
Sam Flamini
Nico Neven
Jason has built search and recommendation systems for the past 6 years. He has consulted and advised a dozens startups in the last year to improve their RAG systems. He is the creator of the Instructor Python library.
Join an upcoming cohort
Cohort 3
$1,800
Dates
Payment Deadline
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
Sign up to be the first to know about course updates.
Join an upcoming cohort
Cohort 3
$1,800
Dates
Payment Deadline