Llamaindex

The first post covered architecture. Here the focus shifts to data: how to generate a realistic product catalog at scale, why description quality matters for RAG, and how the ingestion pipeline embeds everything into ChromaDB. The pipeline produced 1180 products with rich descriptions, embedded them in 39 seconds, and returned retrieval results that actually held up. The archetype strategy Writing 1180 product descriptions by hand is infeasible. Having Claude write them one-by-one is slow and produces inconsistent output. The solution: archetype-based generation. ...

I built Ozark Ridge, a mock outdoor gear retail site with AI-powered product search and a Rufus-style product assistant. The project exists to demonstrate RAG (Retrieval-Augmented Generation) in a realistic e-commerce context. This is the first post in a series documenting the build. This one covers the architecture and stack decisions. Later posts cover the RAG pipeline, keyword vs semantic search comparison, and building the AI assistant. What it does Two features: ...