Boost Search Accuracy: Vectors & Embeddings (Part 3)

Description

Struggling with fuzzy or irrelevant AI search results? You’re not alone. Search is the backbone of knowledge discovery in every organization, yet too often results are imprecise and unhelpful. In Part 3 of this 3-part series, Calvin Hendryx-Parker, CTO of Six Feet Up and AWS Hero, explains how to dramatically improve accuracy by storing multiple embeddings per object and weighting them for maximum precision. You’ll learn: - Why a single “blob” vector per object limits accuracy. - How to embed multiple features (titles, descriptions, metadata) separately. - How weighting vectors improves semantic matching. - How this approach enables flexible, accurate AI-powered search. Whether you’re building internal knowledge bases, e-commerce platforms, or data-driven apps, embedding multiple vectors per object ensures your AI-powered search delivers reliable, nuanced, and context-aware results. 🔍 How to Build Text Similarity with Embeddings in Django → https://sixfeetup.com/blog/implement-text-similarity-with-embeddings-in-django 👉 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