aiqbench analysis
This piece argues that AI can now draft most of a semantic model, shifting data modelers toward reviewing AI output. Its key evidence is a benchmark where Claude Sonnet 5 answered 93% of answerable questions correctly, but the misses ran without errors, so they are hard to catch. The vendor-authored claims about the benchmark and the role shift are preliminary, so readers should weigh them against independent, reproducible evaluations.
Stop Writing Your Semantic Model. Start Reviewing It.
Executive summary AI tools, like AtScale’s CoModel, can now draft most of a semantic model, and that will change the data modeler’s job. Claude Sonnet 5, working through a semantic layer that AI generated and […] The post Stop Writing Your Semantic Model. Start Reviewing It. appeared first on AtScale.
AI tools, like AtScale’s CoModel, can now draft most of a semantic model, and that will change the data modeler’s job. Claude Sonnet 5, working through a semantic layer that AI generated and engineers then tuned, answered 296 of the 317 answerable questions correctly (93%, best of three runs). The misses we examined ran without an error, which is what makes them hard to catch. This post covers what that means for data modelers and why reviewing AI-drafted definitions should be a vital component of their role. AI can now draft the model. Data modelers can spend less time writing definitions and more time reviewing what AI drafts. AI’s wrong definitions look correct. Several incorrect AI-generated definitions ran without flagging an error in AtScale’s preliminary BIRD-Interact benchmark runs. AI semantic…
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