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Most people think of AI in terms of ChatGPT or Claude. But one of the most powerful (and overlooked) building blocks of AI is vectors and embeddings. This is Part 1 of a 3-part series where Calvin Hendryx-Parker, CTO of Six Feet Up and AWS Hero, explains how embeddings are transforming the way businesses extract value from unstructured data. You’ll learn: - What vectors and embeddings are, and how they fit into the AI landscape. - Why embeddings matter for turning text into machine-readable meaning. - When to use embeddings instead of large language models (LLMs). - How vectors power semantic search, similarity matching, and knowledge discovery. - Real-world use cases where embeddings outperform LLMs, such as finding needles in a haystack of organizational content. If your organization is drowning in unstructured data (documents, emails, logs, or product catalogs) traditional search won’t cut it. LLMs may also fall short when your data is inconsistent. Vectors and embeddings allow you to create a semantic map of your information, making it possible to connect ideas, find patterns, and surface insights faster and more accurately. 📖 Case Study: Learn how Six Feet Up helped a client extract gold from millions of datapoints → https://sixfeetup.com/projects/extracting-gold-from-millions-of-datapoints 👉 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