TLDR Tech / Data
SQL Is Solved. Here's Where Chat-BI Still Breaks
Empirical testing of agentic chat-BI systems using BIRD and DABStep benchmarks showed high SQL generation accuracy (over 70% correct on BIRD) but revealed critical failure points: ambiguous metric definitions, out-of-scope questions, and common-sense gaps. While context and rule files help, they can lead to compounding errors and overfitting with increasing complexity. Reliable systems require iterative human-in-the-loop evaluation, structured error classification, deterministic metric definitions, and reproducible CI testing.
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