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.

6 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.

What You’ll Learn, Week-by-Week

Week 1: Fundamentals & Synthetic Data

Establish a repeatable system for evaluating and improving RAG

Generate synthetic datasets for fast, meaningful experiments

Master precision, recall, and essential retrieval metrics

Lay the groundwork for a self-reinforcing improvement cycle

Week 2: Segmentation & Analysis

Break down complex query patterns and user behaviors

Spot inventory gaps and capability shortfalls quickly

Use clustering, classification, and dashboards to track progress

Set improvement priorities that deliver maximum impact

Week 3: Structured Data & Multimodality

Extract and leverage metadata for more accurate retrieval

Handle images, documents, and tables without losing quality

Create specialized indexes and blend them effectively

Apply earlier techniques to broaden RAG capabilities

Week 4: Query Routing & Tool Selection

Integrate multiple search methods into a single coherent product

Route queries intelligently using parallel function calls

Turn tool selection into a quantifiable, improvable task

Secure higher success probabilities end-to-end

Week 5: Representations & Fine-Tuning

Move beyond generic embeddings into fine-tuned, domain-specific vectors

Develop re-rankers that drastically improve retrieval quality

Discover how small amounts of targeted data can yield big gains

Shorten the experimentation cycle for continuous improvement

Week 6: Product UX & Feedback Loops

Design user interfaces that collect feedback effortlessly

Use streaming and visual hints to make latency feel negligible

Implement citations, follow-up actions, and chain-of-thought prompting

Add validators and checks to ensure reliability at scale

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.

Exclusive Bonuses ($1,500+ Value)

$500 Cohere credits

$200 LanceDB credits + free Lance Cloud access

$500 Modal Labs credits

6 months of Notion AI Plus

3 months of Braintrust access ($250 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

You Only Pay After Acceptance & Full Refund Guarantee

We’re so confident in the course’s value that if you don’t see meaningful improvements in your product’s performance within five 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.