Don’t Use Default Models: Vectors & Embeddings (Part 2)

Description

Choosing the right embedding model is critical to the success of your AI project. In Part 2 of this 3-part series, Calvin Hendryx-Parker, CTO of Six Feet Up and AWS Hero, explains why default embeddings give you generic results, and how to choose or fine-tune models that deliver real business value. You’ll learn: - Why default embeddings are only “good enough” and rarely optimized for your data. - How domain-specific embeddings (finance, healthcare, addresses, etc.) improve accuracy and outcomes. - The role of open-source communities like Hugging Face in publishing and benchmarking embedding models. - How to evaluate embedding performance and align models with your use cases. - Why ongoing testing and iteration are essential for production-grade AI. If your AI strategy relies on off-the-shelf defaults, you’re leaving accuracy, efficiency, and trust on the table. Embedding model choice directly impacts the quality of your vector store and semantic search — core infrastructure for enterprise AI. ✨ Dive deeper: Calvin’s All Things Open talk, A Playbook for AI Adoption → https://sixfeetup.com/company/news/all-things-open-ai-a-playbook-for-ai-adoption 👉 Follow Calvin Hendryx-Parker, Six Feet Up CTO and AWS Hero, on LinkedIn for more insight: https://www.linkedin.com/in/calvinhp/ #AI #Embeddings #VectorSearch #SoftwareDevelopment