vector embeddings
vector embeddings at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Explore vector embeddings through an interactive Wordle-style guide”, “Build and benchmark production RAG with fully open models”, and “How DoorDash builds agentic AI from legacy prediction models”. Vector embeddings don't have to be abstract. Building a RAG system that actually works in production takes more than stitching together a vector database. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every vector embeddings story on Beyond Market Intelligence, newest first.
Explore vector embeddings through an interactive Wordle-style guide
Vector embeddings don't have to be abstract. Machine learning engineer Abdur-Rahman Butler built an interactive guide that lets you play with them directly. The centerpiece is SemanticGuessr, a Wordle-meets-GeoGuessr game where you triangulate a hidden word on a 2D semantic map of 43,339 terms. It's challenging at first, semantic space rarely matches intuition, but addictive once you start throwing vector equations at it. For deeper context on how words map into learned vector space, check out our related guide on word embeddings.
Build and benchmark production RAG with fully open models
Building a RAG system that actually works in production takes more than stitching together a vector database. This workshop on August 29, led by Ben Auffarth of Chelsea AI Ventures, tackles the messy parts head-on: hybrid retrieval, reranking, and guardrails built in from the start. It's refreshing to see evaluation with RAGAS and real cost benchmarking treated as essentials, not afterthoughts. If you're tired of demo-grade AI, this hands-on session is worth exploring.
How DoorDash builds agentic AI from legacy prediction models
Sudeep Das walks us through DoorDash's move from one-shot predictions to a platform built for context-aware recommendations. It's a shift from static models to something more adaptive, using language-native memory and semantic IDs to keep pace with real consumer behavior. The grounded search piece matters too, as it ties relevance directly to conversion. For anyone wrestling with how to scale AI beyond isolated tasks, this is a practical look at what agentic systems actually require.