Rob Quiros PRO
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AI & ML interests
Knowledge Graphs, Data Governance, Enterprise AI
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3 days ago
Is Your Machine Learning Model Bingeing on Junk Data?
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an
update
5 days ago
Most enterprise AI is still guessing.
We boost attention on repeated tokens.
We connect chunks that look similar.
We extract “semantic” relationships from isolated sentences.
But we’re missing the point.
Business meaning doesn’t come from what data looks like. It comes from how it’s used.
🧩 A contract clause copied into a policy doesn’t mean the same thing.
📄 A word like “status” changes meaning from Jira to FDA filings.
📊 A graph without axes labelled is just noise.
Until enterprises can ground context window data in metadata, workflow, and policy, we’re just building smarter approximations.
Check out my latest blog here:
https://www.caber.com/blog/e74c56ee-a15b-443a-be7b-3f3b82c68406
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5 days ago
A little structure to your context window data can go a long way.
15% better answer quality ⚡ 35% ⚡ faster—including the time it took the tools to generate the graphs and schemas‼️
The pace of improvement in AI coding applications has been incredible, even so a little 💪🏻context engineering💪🏻 often matters more than the app or model.
📊 Real examples from a recent codebase cleanup we added:
🥇 Dependency graphs showing what calls what
🥈 Schemas for Pandas DataFrames
and saw better answer quality delivered ⚡ 35% ⚡ faster—including the time it took the tools to generate the graphs and schemas‼️
See the post for details on what we did: https://www.caber.com/blog/12744d4b-1d65-4e6c-886d-978c0f1317f5
🔍 This applies beyond code: documentation hierarchies, data lineage, system architectures. Data relationships provide context that unlocks reasoning.
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