RAGs to Riches — Economics of Retrieval-Augmented Generation

August 21, 2025
Toledo Tech Loft

Toledo Codes hosted an in-depth exploration of Retrieval-Augmented Generation (RAG) systems, featuring Justin Beaudry's comprehensive presentation on building applications that leverage the power of semantic search and large language models. This session demystified the core components of RAG architectures and provided practical insights for developers, product managers, entrepreneurs, students, and anyone curious about AI technology.

What Was Covered

The RAG Foundation

Understanding how RAG systems combine the power of retrieval mechanisms with generative AI to create more accurate, contextual, and trustworthy applications, and how to build and maintain an effective corpus that serves as your application's knowledge base.

Search & Ranking Deep Dive

Mastering the mechanics of semantic search, including cosine similarity and other ranking algorithms, and how to optimize retrieval performance so systems return the most relevant information for any query.

The Art of Chunking

The critical decisions around text chunking strategies — optimal chunk sizes, the importance of chunk overlap, and how these choices impact both retrieval quality and system performance.

Embedding Models Decoded

Navigating the landscape of embedding models: what embeddings actually represent, how popular models compare, and how to select the right model for specific use cases and performance requirements.

Beyond Basic RAG: Knowledge Graph Augmentation

Taking RAG implementation further by incorporating knowledge graphs, and how graph-based representations can enhance context understanding, improve relationship mapping, and enable more sophisticated query responses.

Key Takeaways

Economic Considerations: Understanding the cost implications and trade-offs in RAG system design
Architecture Patterns: Best practices for building scalable and maintainable RAG applications
Performance Optimization: Strategies for improving retrieval accuracy and response quality
Real-world Applications: Practical examples of successful RAG implementations