Data Context Is the Missing Layer of GenAI
summary
Claim: GenAI fails in production not because of models but because of a missing context layer that supplies meaning, lineage, ownership. The concrete fix is a data catalog/semantic layer (DataGalaxy-style) surfaced via an MCP server so agents retrieve certified definitions and rules. Examples: “active users” guesses return different counts; revenue formula from a blind model is useless for an IRR business; HubSpot blocked-deals is 20% without context vs 15.9% with cataloged definitions. Start with owned, high-value terms; don’t dump uncertified metadata.
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chapters (13)
- 0:00Data context in GenAI
- 1:12GenAI fails in production
- 4:25Agentic AI and risks
- 6:32Missing layer under GenAI
- 8:20Missing context examples
- 11:12Five components of context
- 12:59Semantic layers for AI agents
- 15:48Bridging data and AI
- 16:51MCP standard for AI
- 18:39Demo: context vs no context
- 21:54Lessons from context
- 22:36AI adoption: what works
- 24:23Conclusion
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