Are you struggling to scale your RAG application beyond the prototype stage?

Feeling overwhelmed by competing priorities and limited resources?

In just 6 weeks, you'll learn to:

* Optimize search quality and latency

* Design robust feedback loops for continuous improvement

* Implement data-driven strategies for maximum impact

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

- Instructor's real-world experience provides immediately applicable insights

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

Why does my team need this?

- Align your team 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

- Develop skills to justify AI investments to leadership

- Learn from real-world case studies across industries

- Connect with professionals facing similar challenges

- Acquire skills applicable to all AI initiatives, not just RAG

About the Instructor

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. Currently, he leads runs this own consulting studio where he's work with many companies training teams to build rag solutions across private equity, financial services, construction, web crawling, sales, marketing, personal agents with a focus on

* Designing self-improving AI systems with robust feedback loops, creating valuable data flywheels.

* Developing and implementing Vision/Text based search systems that integrate seamlessly into existing product ecosystems.

* Crafting evaluation frameworks and fine-tuning algorithms for search and recommendation systems.

* Making strategic AI research bets and evaluating vendors to drive innovation and scalable growth.

Week 0: Fundamental Biases in AI Engineering

Recognizing and Mitigating Blind Spots in Development

* Understand common biases in AI system development, such as intervention bias and absence blindness

* Learn techniques to identify and mitigate these biases in RAG systems

* Develop strategies for comprehensive system evaluation and blind spot detection

* Explore case studies of bias-related failures in AI systems and their remedies

Week 1: Kickstarting the Data Flywheel

Leveraging Synthetic Data for Evals and Data Augmentation

* Implement the RAG System Inference Flywheel concept

* Create robust evaluation pipelines using synthetic data

* Develop scalable datasets for continuous system improvement

* Master techniques for fast, iterative improvement cycles using precision and recall metrics

* Distinguish between leading and lagging metrics to set actionable goals

Week 2: Finetuning Search and Hard Negative Mining

Optimizing Representations for Precision and Recall

* Understand the principles of representation learning for search

* Implement techniques for hard negative mining to improve search quality

* Develop strategies for fine-tuning embedding models and re-rankers

* Learn to balance precision and recall in search optimization

* Explore advanced techniques like contrastive learning for search improvement

Week 3: Decomposing Query Types and Identifying Bottlenecks

Prioritizing Investments for Maximum Impact

* Implement classification systems for query segmentation

* Conduct thorough bottleneck analysis in RAG systems

* Apply data-driven approaches to detect concept drift and adapt systems dynamically

* Understand inventory vs capability segments and develop strategies for unsupervised topic discovery

* Create comprehensive dashboards for visualizing query patterns and system performance

Week 4: Navigating Multimodal RAG

Tailoring Approaches for Documents, Images, and Tables

* Design and implement search strategies for diverse content types (images, documents, tables, text-to-SQL)

* Develop metrics to measure and improve search quality across different modalities

* Learn techniques for extracting structured data from various content types

* Implement specialized indexing and retrieval methods for each content type

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

Week 5: Efficient Routers and Index Fusion

Designing Scalable Systems for Complex Queries

* Build intelligent routers for multi-index RAG systems

* Implement sophisticated query understanding techniques

* Develop strategies for efficient index fusion and result aggregation

* Evaluate and balance trade-offs between search architectures for latency, cost, and accuracy

Week 6: Enhancing User Experience and Feedback Loops

Strategies for Latency Perception and Continuous Improvement

* Design RAG products that effectively collect user feedback

* Implement streaming strategies to improve perceived latency

* Create intuitive UI components for citations and user interaction

* Develop strategies for handling negative examples and continuously improving performance

* Implement validators and monologue techniques to enhance response quality

This comprehensive playbook will enable you to deliver consultant-level value, leading your team to results through structured experimentation.

💡 COURSE PREREQUISITES

You should NOT take this course if:

* You work on non-software products (e.g. hardware, pharmaceuticals, deep climate tech, defense tech, etc.)

* If you have not tried to build a RAG application in the past, this course is about improving systems as we move from prototype to production

Don't do it alone - be part of a small cohort of other teams shipping real applications.

You pay only after your application is approved.

Get these free bonuses (over $1500 in value):

• $500 Cohere credits (Jason uses Cohere rerankers in every single RAG product he's build or adviced)

• $200 LanceDB credits and free access to Lance Cloud

• $500 in Modal Labs credits (useful for experimenting with embedding fine-tuning)

• 6 months free Notion AI Plus (get experience with more RAG products)

• 3 months Braintrust access ($250 value)

🚀 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.