Jason Liu – Systematically Improving RAG Applications

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Jason Liu – Systematically Improving RAG Applications

Build Retrieval-Augmented Generation (RAG) systems that are smarter, faster, and more reliable.

In Systematically Improving RAG Applications, Jason Liu walks you through a structured process for diagnosing performance issues,

refining data pipelines, and ensuring your AI-powered applications deliver consistent, high-quality results.

Why This Program Stands Out

Systematic Approach – No guesswork; follow a proven improvement cycle.

Hands-On Focus – Code, test, and iterate alongside the lessons.

Applicable Across Domains – Whether in search, chatbots, or analytics, the principles transfer.

Future-Proof – Stay ahead with techniques adaptable to new AI models and architectures.

Key Competencies You’ll Master

RAG Fundamentals & Architecture – Understand the core building blocks and how each affects performance.

Data Preparation & Cleaning – Improve retrieval accuracy through smarter preprocessing.

Prompt Engineering for RAG – Design prompts that integrate seamlessly with retrieved data.

Evaluation & Benchmarking – Implement robust methods for measuring output quality.

Optimization Loops – Create feedback-driven improvement systems for ongoing refinement.

Scaling Considerations – Ensure stability when handling large datasets and high request volumes.

Ideal Participants

AI Developers – Building or maintaining RAG-based products.

Machine Learning Engineers – Looking to enhance retrieval pipelines and model integration.

Data Scientists – Wanting deeper insight into AI application deployment.

Tech Founders & Product Managers – Needing a framework to guide AI feature development.

Course Resources & Tools

Step-by-Step Implementation Guides – Turn theory into code with detailed walkthroughs.

Code Samples & Templates – Pre-built snippets to accelerate development.

Performance Dashboards – Tools for tracking, visualizing, and analyzing improvements.

Case Studies – Learn from real-world RAG optimization successes and failures.