[morisil]

1. LLMs excell at extracting facts from the context. Storing them as a subject-predicate-object relationships is "natural" for graph databases. Doing it right, so that this knowledge can be utilized more efficiently than any RAG, requires sophisticated context engineering, for example to avoid duplicates and keep consistent vocabulary for relationships, but it is totally achievable and the quality of automatically extracted knowledge can be almost spotless, especially if an LLM can also decide on generating parallel embeddings as a semantic search entry point for graph traversal.

2. Writing cypher queries is a job I would never like to have as a human. But LLMs love it, so that an agent can do an ad hoc data science for every single problem. Especially while being aware which criteria were used for graph construction. It is worth ditching things like MCP in favor of tool graph-like solutions. For this purpose I developed my own DSL which only LLM speaks in internally. The effects are mind-blowing.