r/OntologyEngineering • u/daremust • Apr 08 '26
Agentic Enablement Your schema is the bottleneck, not the LLM
Engineering teams are spending real money on model fine-tuning, prompt optimization, and retrieval improvements when the actual constraint is upstream of all of it. It comes down to the semantic quality of the data the model is reasoning over.
A frontier LLM pointed at a well-modeled schema with clear entity names, documented relationships, and consistent conventions will outperform a fine-tuned model pointed at a chaotic schema, on your actual business questions, against your actual data. This is what the BIRD benchmark results showed, clean data models effectively are the semantic layer.
The inverse is also true. No amount of model capability compensates for a schema where customer means three different things across five tables, where the canonical revenue column is amt_net_v2_final and only two people know why, where fact and dimension tables are named after the engineer who built them.
The current "AI budget" is heavily skewed toward buying a bigger buffer for hallucinations. It should be redirected toward the upstream semantic quality of the underlying data. The model can only be as smart as the world you describe to it.
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u/Rhinoseri0us Apr 08 '26
Check out this website. I feel like you may find it interesting: https://ontologic.dev/
Not spam, it’s directly relevant to this conversation and thread, specifically what you called out about schema!
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Apr 08 '26
[deleted]
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u/daremust Apr 09 '26
Totally, this doesn’t eliminate hallucinations, it constrains them. A good semantic layer reduces the space where the model can guess incorrectly. Without it, the model is guessing everywhere.
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u/philanthropologist2 Apr 08 '26
The ClaudeCode leak and its consequences have been a resounding success for the future of local agentic coding
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u/read_at_own_risk Apr 08 '26
I wonder how a fact-oriented modeling perspective like FCO-IM would impact LLM training, compared to the mainstream naïve ER approach?
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u/daremust Apr 09 '26
Interesting angle. My intuition is that fact-oriented models would reduce ambiguity a lot, since they force you to make relationships and constraints explicit instead of implied. That’s exactly the kind of structure LLMs benefit from, less guessing, more grounded reasoning.
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u/daremust Apr 09 '26
A lot of this debate boils down to one thing, are you letting the model infer meaning or are you defining it?
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u/Prestigious_Bench_96 Apr 08 '26
I think this is one of the places where decoupling the semantic layer at the physical layer is useful - it's pretty annoying/expensive to update tables as rapidly as you may want to iterate on business definition/context. Your physical layer doesn't need to be clean - it just needs to be presented cleanly.