Become the RAG Product Thinker Your Company Needs

In the rapidly evolving world of AI, companies are in a fierce arms race. They're scrambling to find developers and product leaders who can successfully incorporate AI into their products before competitors do it better.

Are you ready to be that leader?

The Challenge: Navigating the RAG Black Box

You're working on AI-powered applications, but:

* Time and resources are limited

* The best path forward isn't clear

* The inner workings of your product are shrouded in uncertainty

The Solution: Develop the "Product Sense" for AI

In just 6 weeks, learn to:

* Optimize search quality and latency

* Design robust feedback loops for continuous improvement

* Implement data-driven strategies for maximum impact

* Navigate AI product decisions in uncertain environments

Course Breakdown: From Fundamentals to Advanced Techniques

Week 1: Fundamentals and Synthetic Data Generation

* Understand the importance of having a system for evaluating and improving RAG

* Learn to generate synthetic data for fast evaluations

* Master precision and recall metrics for retrieval evaluation

* Distinguish between leading and lagging metrics

* Implement the RAG System Inference Flywheel

Week 2: Segmentation and Analysis

Learn the importance of segmenting queries and users

Differentiate between inventory issues and capability issues

Use clustering and classification to identify query types and segments

Set up dashboards to monitor query distributions and performance over time

Prioritize improvements based on impact, volume, and likelihood of success

Week 3: Structured Extraction and Multimodality

Learn techniques for handling different data types: documents, images, tables

Extract metadata and generate synthetic data/summaries to improve search

Implement approaches for document search, image search, and querying tables

Apply Week 1-2 techniques to evaluate and improve individual indices

Balance trade-offs between generalized and specialized approaches in multimodal RAG

Week 4: Query Routing and Tool Selection

Combine multiple search indices into a cohesive application

Implement parallel function calling and API gateways to route queries

Evaluate tool selection as a classification task

Avoid data leakage when generating examples

Break down probability of success into tool selection and retrieval components

Week 5: Representations and Fine-tuning

Understand limitations of pre-trained embedding models

Learn the importance of fine-tuning embeddings and re-rankers on domain-specific data

Collect relevance data to create triplet training examples

Explore benefits of fine-tuning a single model across multiple tasks

Discover how relatively small amounts of data can lead to significant improvements

Week 6: Product Design and User Experience

Implement techniques for collecting user feedback

Design streaming strategies to improve perceived latency

Create UI components for rendering citations and follow-up actions

Apply prompting techniques like chain-of-thought and monologues

Add validators to improve reliability and response quality

Develop strategies for handling negative examples

Why This Course, Why Now?

RAG is becoming essential for competitive AI integration

Focus is shifting from basic implementation to performance optimization

Core principles of effective RAG systems are crystallizing

Early adopters gain significant market advantages

What You'll Learn

Cold start your evaluation pipeline for retrieval

Understand the limitations of embedding models and how to think about rerankers and fine-tuning

Master retrieval metrics and use them to quickly run experiments

Identify high-impact tasks and prioritize effectively

Make informed tradeoffs and choose relevant metrics

Focus on what matters most in AI product development

Why Your Team Needs This

Align on RAG best practices

Save months of trial and error

Build scalable systems that prevent future rewrites

Foster a data-driven culture of continuous improvement

Bridge gaps between technical and business teams

Gain competitive edge in AI implementation

Justify AI investments to leadership

Learn from real-world case studies across industries

About Your Instructors

Jason Liu is a seasoned machine learning consultant with experience at Stitchfix, and Meta, Stitch Fix, and dozens more companies, ranging from startups to Limitless to Larger enterprises like Hubspot and Zzapier. When companies struggle to make progress, they hire Jason help their AI teams find "the path" forward.

Jason Liu is a machine learning engineer and data scientist with 8 years of experience in building recommendation systems and multi-modal semantic search products at Stitchfix

What You'll Get

A community of other operators and AI product thinkers

Hands-on experience with real-world RAG applications

Strategies for continuous system improvement

Skills applicable to all AI initiatives, not just RAG

Access to instructors' real-world experience and immediately applicable insights

Bonus Offers (Over $1500 in Value!)

$500 Cohere credits

$200 LanceDB credits and free access to Lance Cloud

$500 in Modal Labs credits

6 months free Notion AI Plus

3 months Braintrust access ($250 value)

Who Should Apply?

This course is perfect for you if:

You're working on software products incorporating AI

You've built a RAG prototype and want to move to production

You're looking to develop a "product sense" for AI applications

Won't a lot change by February?

Course focuses on enduring principles, not just current tools

You'll learn to evaluate and integrate new technologies rapidly

Strategies taught focus on ongoing system optimization

By February, you'll be positioned to leverage new developments immediately

Course content will be updated to reflect any significant changes

Skills developed are foundational and will remain relevant

Don't Miss Out!

🚀 Limited Spots Available 🚀

Our small-group cohorts fill up fast. Don't wait to level up your RAG skills.

Remember, you only pay after your application is approved. We're so confident in the value of this course that we offer a full refund if you don't see meaningful improvements in your processes within 5 weeks.

Join a small cohort of other teams shipping real applications and take your RAG skills to the next level. Don't do it alone - be part of a community of professionals facing similar challenges and working towards AI excellence.