Semantic Embeddings: How We Can Explicitly Use the Latent Space

Semantic embeddings are ubiquitous in AI development, yet it is rare for AI developers to spend much time thinking about them among all of the other processes that AI projects entail. Are embeddings a "solved" problem? Are they just a process we run during document preparation for AI use, or is there more that we might gain by incorporating embedding strategies into AI and Data Governance?

In this discussion, we will explore what semantic embeddings truly are, how they are relevant for modern AI development, and what AI developers and strategists can potentially gain from thinking seriously about how we can use semantic embedding as a tool unto itself.

What kinds of problems can smarter embedding strategies help solve?
Whether you're building AI solutions or making decisions about them, you've likely run into challenges like these:

• Your RAG chatbot retrieves documents that look relevant but miss the point of the question
• Your AI gives confident but wrong answers because it pulled the wrong context
• You need to organize thousands of documents by topic, but manual tagging doesn't scale