Systematically Improving RAG Applications

A comprehensive educational resource teaching data-driven approaches to building and improving Retrieval-Augmented Generation systems that get better over time. Learn from real case studies with concrete metrics showing how RAG systems improve from 60% to 85%+ accuracy through systematic measurement and iteration.

What You'll Learn

Transform RAG from a technical implementation into a continuously improving product through:

• Data-driven evaluation: Establish metrics before building features
• Systematic improvement: Turn evaluation insights into measurable gains
• User feedback loops: Design systems that learn from real usage
• Specialized retrieval: Build purpose-built retrievers for different content types
• Intelligent routing: Orchestrate multiple specialized components
• Production deployment: Maintain improvement velocity at scale

Real Case Studies Featured

Legal Tech Company: 63% → 87% accuracy over 3 months through systematic error analysis, better chunking, and validation patterns. Generated 50,000+ citation examples for continuous training.

Construction Blueprint Search: 27% → 85% recall in 4 days by using vision models for spatial descriptions. Further improved to 92% for counting queries through bounding box detection.

Feedback Collection: 10 → 40 daily submissions (4x improvement) through better UX copy and interactive elements, enabling faster improvement cycles.

The RAG Flywheel

The core philosophy centers around the "RAG Flywheel" - a continuous improvement cycle that emphasizes:

1. Measure: Establish benchmarks and evaluation metrics
2. Analyze: Understand failure modes and user patterns
3. Improve: Apply targeted optimizations
4. Iterate: Continuous refinement based on real-world usage

Repository Structure

.
├── docs/            # Complete workshop series (Chapters 0-7)
│   ├── workshops/   # Progressive learning path from evaluation to production
│   ├── talks/       # Industry expert presentations with case studies
│   ├── office-hours/# Q&A summaries addressing real implementation challenges
│   └── misc/        # Additional learning resources
├── latest/          # Reference implementations and case study code
│   ├── case_study/  # Comprehensive WildChat project demonstrating concepts
│   ├── week0-6/     # Code examples aligned with workshop chapters
│   └── examples/    # Standalone demonstrations
├── data/            # Real datasets from case studies and talks
└── mkdocs.yml       # Documentation configuration

Learning Path: Workshop Chapters

The workshops follow a systematic progression from evaluation to production:

Chapter 0: Beyond Implementation to Improvement

Mindset shift from technical project to product. See how the legal tech company went from 63% to 87% accuracy by treating RAG as a recommendation engine with continuous feedback loops.

Chapter 1: Starting the Data Flywheel

Build evaluation frameworks before you have users. Learn from the blueprint search case: 27% → 85% recall in 4 days through synthetic data and task-specific vision model prompting.

Chapter 2: From Evaluation to Enhancement

Turn evaluation insights into measurable improvements. Fine-tuning embeddings delivers 6-10% gains. Learn when to use re-rankers vs custom embeddings based on your data distribution.

Chapter 3: User Experience (3 Parts)

3.1 - Feedback Collection: Zapier increased feedback from 10 to 40 submissions/day through better UX copy
3.2 - Perceived Performance: 11% perception improvement equals 40% reduction in perceived wait time
3.3 - Quality of Life: Citations, validation, chain-of-thought delivering 18% accuracy improvements

Chapter 4: Understanding Users (2 Parts)

4.1 - Finding Patterns: Construction company discovered 8% of queries (scheduling) drove 35% of churn
4.2 - Prioritization: Use 2x2 frameworks to choose what to build next based on volume and impact

Chapter 5: Specialized Retrieval (2 Parts)

5.1 - Foundations: Why one-size-fits-all fails. Different queries need different approaches
5.2 - Implementation: Documents, images, tables, SQL - each needs specialized handling

Chapter 6: Unified Architecture (3 Parts)

6.1 - Query Routing: Construction company: 65% → 78% through proper routing (95% × 82% = 78%)
6.2 - Tool Interfaces: Clean APIs enable parallel development. 40 examples/tool = 95% routing accuracy
6.3 - Performance Measurement: Two-level metrics separate routing failures from retrieval failures

Chapter 7: Production Considerations

Maintain improvement velocity at scale. Construction company: 78% → 84% success while scaling 5x query volume and reducing unit costs from $0.09 to $0.04 per query.

• Part 1: Understanding different content types
• Part 2: Implementation strategies
• Topics:
  • Working with documents, images, tables, and structured data
  • Metadata filtering and Text-to-SQL integration
  • PDF parsing and multimodal embeddings

Week 6: Architecture & Product Integration

Technologies & Tools

The workshops use industry-standard tools for production RAG systems:

• LLM APIs: OpenAI, Anthropic, Cohere
• Vector Databases: LanceDB, ChromaDB, Turbopuffer
• Frameworks: Sentence-transformers, BERTopic, Transformers, Instructor
• Evaluation: Synthetic data generation, precision/recall metrics, A/B testing
• Monitoring: Logfire, production observability patterns
• Processing: Pandas, SQLModel, Docling for PDF parsing

Documentation

The /docs directory contains comprehensive workshop materials built with MkDocs:

Content Overview

• Workshop Chapters (0-7): Complete learning path from evaluation to production
• Office Hours: Q&A summaries addressing real implementation challenges
• Industry Talks: Expert presentations on RAG anti-patterns, embedding performance, production monitoring
• Case Studies: Detailed examples with specific metrics and timelines

Core Philosophy

1. Product mindset: RAG as evolving product, not static implementation
2. Data-driven improvement: Metrics and feedback guide development
3. Systematic approach: Structured improvement processes over ad-hoc tweaking
4. User-centered design: Focus on user value, not just technical capabilities
5. Continuous learning: Systems that improve with every interaction

Build and view documentation:

mkdocs serve  # Local development with live reload
mkdocs build  # Static site generation

Getting Started

Important: Use the latest/ Directory

⚠️ Always work in the latest/ directory for the most current course content.

The cohort_1/ and cohort_2/ directories contain materials from previous course iterations and are kept for reference only. All new development and course work should be done in latest/.

Prerequisites

• Python 3.11 (required - the project uses specific features from this version)
• uv package manager (recommended) or pip

Installation

1. Clone the repository

2. Navigate to the latest/ directory:

   cd latest/
   

3. Install dependencies:

   # Using uv (recommended)
   uv install
   
   # Or using pip
   pip install -e .
   

4. Start with week0/ for the most up-to-date content

5. Follow the notebooks in sequential order within each week

6. Reference the corresponding book chapters in /docs for deeper understanding

Code Quality

Before committing changes, run:

# Format and fix code issues
uv run ruff check --fix --unsafe-fixes .
uv run ruff format .

Philosophy

This course emphasizes:

• Systematic Improvement: Data-driven approaches over guesswork
• Product Thinking: Building RAG systems that solve real problems
• Practical Application: Real-world datasets and examples
• Evaluation-First: Measure before and after every change
• Continuous Learning: The field evolves rapidly; the flywheel helps you adapt

Additional Resources

• Industry talk transcripts in /data/
• Office hours recordings summaries in /docs/office_hours/
• Advanced notebooks in /latest/extra_kura/ for clustering and classification topics
• Complete case study implementation in /latest/case_study/

License

This is educational material for the "Systematically Improving RAG Applications" course.