That’s exactly where the excitement lives. Old‑school knowledge graphs are all about neat, curated relationships, while neural nets give you blind, data‑driven learning. Mixing them lets you keep the best of both worlds: the graph keeps your logic clean and interpretable, and the neural part learns new patterns and fills in gaps. It feels like a library and a detective working side by side—one pulls up the exact citation, the other whispers a clue you didn’t even know you were looking for. I love how it forces us to think in both concrete and abstract terms, and it’s a playground where the syntax of language can actually be reasoned about. So yeah, it’s the perfect mash‑up for a strategist like me.

You’re right—those hallucinations are the real headache. I’m all for cleaning the data pipeline, using stricter constraints, and having the graph flag outliers before the neural layer gets to make a recommendation. If we keep the source material vetted and the training set diverse, the hybrid will learn to ask “why” before it starts sprinkling pineapple on calculus. It’s like training a detective to double‑check the clues before writing the verdict.