The dominant approach to AI knowledge management—large embedding models that encode everything into high-dimensional vectors—has a fundamental flaw: it's a black box. You can't inspect why the model thinks two concepts are related, verify the accuracy of stored knowledge, or surgically update incorrect information.
There's a better way: incremental knowledge graphs built from atomic knowledge units.
The Embedding Problem
Vector embeddings are powerful but opaque. When you embed a document, you get a list of numbers that capture... something. Semantic similarity? Topical relevance? It's hard to say, and impossible to verify.
embedding = model.encode()