Systematically Improve Your RAG Applications¶

Top-rated AI course on Maven (4.8/5 stars, 1000+ students)

A 6-week, hands-on certification that takes you from evaluation fundamentals to production-grade RAG systems. Stop guessing. Start measuring.

Enroll on Maven (20% off with code EBOOK) Free 6-Day Crash Course

Trusted By Engineers From¶

Startups: Decagon | Anysphere | DataStax | Timescale

The Problem¶

Most RAG implementations fail because teams focus on model selection and prompt engineering while ignoring the fundamentals: measurement, feedback, and systematic improvement.

Common mistakes:

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 gives you a clear, repeatable process: from collecting the right data and generating synthetic evaluations, to routing strategies, fine-tuned embeddings, and practical UX improvements.

What You'll Learn¶

Week	Topic	Outcome
1-2	Foundations & Evaluation	Build synthetic data pipelines, measure precision/recall
3-4	Retrieval & Routing	Specialized indices, query routing, multi-modal search
5	Fine-tuning	Custom embeddings, re-rankers, 10-30% recall improvements
6	Product & UX	Feedback collection, streaming, validation pipelines

Core skills:

Build proper evaluations with synthetic data
Segment queries to identify high-impact opportunities
Create specialized indices for different content types
Design UIs that generate continuous improvement data
Fine-tune embeddings for your domain

What Makes This Different¶

1. RAG is a recommendation problem, not an AI problem

"RAG is really just a recommendation system squeezed between two LLMs." Focus on what you can control: improve search quality first, generation quality follows.

2. Evaluation-first approach

No more vibe checks. Set up meaningful evaluations, identify high-impact opportunities, and measure every change.

3. Build improvement flywheels

Create systems that get better with every user interaction. Continuous refinement based on real feedback.

What Students Say¶

"As an Applied AI Engineer at Anthropic, I was familiar with all of the standard retrieval methods and RAG papers, but Jason's frameworks helped me operationalize what I'd learned and it's had an incredibly positive impact in my work with customers."

Sam Flamini, Solutions Engineer at Anthropic

"I would put this course on par with a Master's degree at a leading institution. 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 at Omni

"Jason's RAG Flywheel methodology transformed how our team approaches retrieval systems. The structured approach gave us immediate 30% accuracy gains on domain-specific queries."

Chiheb Dkhil, Founder & CTO at 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. You'll get the most from it if you've deployed a RAG system."

Hendrik Reh, Indie Consultant

More Reviews¶

"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." — Uzair Qarni, CTO at Gepeto

"This course is a must-have. The opportunity to learn from experienced practitioners who have navigated the challenges of bringing a product to market is incredibly valuable. Think of it as ML for LLMs." — Paul Perry, CTO at Ampliforce

"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." — Leo Carlsson, Data Scientist at SSAB

"Practical and grounded in actual industry experience... like getting the inside scoop from folks who've been in the trenches." — Ashutosh, Senior Principal Scientist at Adobe

"System-oriented approach... Highly relevant, directly applicable, and saves time in building prototypes." — Mani, Senior Principal Software Engineer at Red Hat

"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." — Nishant Hegde, Staff Analytics Engineer at Netflix

Course Details¶

Format: 6 weeks, online, hands-on

Includes:

12 hours of live instruction
Weekly office hours
Hands-on notebooks with real datasets
Private community of 500+ practitioners
$1,500+ in tool credits (Cohere, LanceDB, Modal Labs)

Guarantee: Money-back if you don't see improvement after 4 weeks.

Not Ready for the Full Course?¶

Start with the free RAG Playbook newsletter. Get bite-sized lessons covering the fundamentals of RAG systems and practical tips for improvement.

Free 6-Day RAG Crash Course

Enroll¶

Enroll on Maven (20% off with code EBOOK)

Get Reimbursed¶

Email Template for Your Manager

Hey {manager},

I've found a course called "Systematically Improving RAG Applications" that would be valuable for our team:

Expert Instruction: Learn from Jason Liu, 8 years experience in recommendation systems and RAG
Practical Application: Hands-on sessions with real datasets and implementation guides
Systematic Approach: Frameworks for continuous improvement, not one-off fixes
Added Value: $1,500+ in tool credits included
Risk-Free: Money-back guarantee if we don't see improvements

The course costs $1,800. I plan to share learnings with the team. More details: https://maven.com/applied-llms/rag-playbook

What do you think?

{Your Name}