Ep75: Versioning AI Models + Define Schema for Better Knowledge Graphs

This week, I prepped for upcoming events, tweaked and strategized some existing processes, and found more data on how defining a schema can produce better knowledge graph construction.

Highlights:

• Prepped for two upcoming events: a Graph RAG Fundamentals training on O'Reilly Learning Platform and a session at a virtual AI Agents conference.
• Updating repositories for the workshop surfaced a chain-reaction lesson: upgrading frameworks leads to data changes, which require config updates, which require prompt rewrites.
• Key takeaway — don't pin your apps to latest for AI models, just as you wouldn't for Docker image tags. Tie to a specific version so updates don't cascade unexpectedly.
• Also revisited my tech blogging workflow and built a template script to eliminate boilerplate setup, shaving time off the writing process without sacrificing the actual content creation.
• New blog post on agents, tools, and MCP published in the process!
• On the Neo4j side, I touched on the Neo4j Educator Program and how learning patterns among new developers are shifting — happy to accept feedback from educators teaching graphs.
• This week's article is Hands-on KG Relation Resolution by Mike Dillinger. It examines knowledge graph construction and why defining a narrowed schema produces cleaner, more understandable graphs. Without boundaries, LLMs and NLP processes generate overly granular, spaghetti-like structures.

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