You're working on a new RAG application in a chaotic environment. Competing priorities and limited resources make progress challenging, with each day feeling like a roll of the dice. Success hinges on finding "the right path" - a sequence of actions for maximum growth in minimal time.

Beyond technical skills, effective AI development requires strategic decision-making and resource allocation.

Build strong fundamentals in building searching systems and improve your RAG systems. I've refined these over 8 years of machine learning engineering and AI consulting. This course will equip you with the skills to:

- Implement robust processes for identifying system bottlenecks

- Significantly enhance your probabilistic systems

- Apply advanced RAG techniques for real-world applications

- Seize this opportunity to transform your RAG capabilities and stay ahead in the rapidly evolving field of applied AI

Week 1: RAG System Foundations

- Create robust evaluation pipelines, develop scalable datasets, and apply the RAG System Inference Flywheel concept

- Recognize and mitigate intervention bias and absence blindness in your RAG systems

- Implement fast, iterative improvement cycles using precision and recall metrics

- Distinguish between leading and lagging metrics to set actionable goals

- Master techniques for continuous improvement and optimization of RAG foundations

Week 2: Query Processing and Intent Analysis

- Implement classification systems for query segmentation and conduct thorough bottleneck analysis

- 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

- Learn advanced techniques for enhancing query understanding and intent analysis

Week 3: Multi-Index Search and Intelligent Routing

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

- Develop metrics to measure and improve search quality across multiple indices

- Build intelligent routers and implement sophisticated query understanding techniques

- Construct and optimize multi-hop agent systems utilizing multiple search indices

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

Week 4: RAG Product Design and User Experience

- Design RAG products that effectively collect user feedback and implement streaming strategies

- Create intuitive UI components for citations and user interaction, enhancing overall UX

- Develop strategies for handling negative examples and continuously improving performance

- Implement validators and monologue techniques to significantly enhance response quality

- Align product features with business objectives while maximizing user satisfaction and engagement

What You'll Learn

After completing this comprehensive RAG course, you'll be equipped to:

- Implement rapid, data-driven iteration cycles using synthetic evaluations and user feedback to identify and address system bottlenecks

- Design intuitive UIs that simultaneously enhance and measure customer satisfaction, optimizing the balance between user experience and data collection

- Develop intelligent query routing and search architectures that maximize performance within resource constraints

- Create compelling, data-backed arguments for system improvements and resource allocation

- Effectively communicate RAG system enhancements and their impact to both technical and non-technical stakeholders

This comprehensive playbook will enable you to deliver consultant-level value, leading your team to results through structured experimentation. We guarantee meaningful improvements in processes in 5 weeks or we'll personally refund you and do a free consultation to help figure out your needs.

Our consulting has achieved:

- 80%+ satisfaction in previously unsuccessful query segments

- 4x increase in feedback volume and 10% boost in CSAT

- Finetune embedding models that outperform closed models by 20-30%

- 2-3x increase in weekly experiments using synthetic data benchmarks

- Real-time monitoring systems revealing new product positioning insights

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

To ensure everyone can be successful, the course requires a short application. 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)