Behind the Curtain: How We Do Eval in Genie

The approach reframes evaluation, guardrails, and feedback loops as core product capabilities, not afterthoughts, to power agentic analytics with AI/BI and Genie. Organizations evaluating AI-driven BI tooling can find a detailed breakdown of criteria and trade-offs in this enterprise analytics platform evaluation guide.

Enterprise analytics demands more than fluent answers. SQL that compiles can still be logically wrong, and a convincing chart can hide a bad schema mapping. Genie counters that risk by constraining generation to the customer's domain, inheriting Unity Catalog permissions, and preferring "I don't know" or a clarifying question over a guess.

Why plausibility is not enough for BI-grade answers

Consumer benchmarks tend to reward coherence and creativity. BI needs correctness, lineage, and access control. Genie treats accuracy, governance, and humility as inseparable: it sees only data the user is entitled to, adheres to governed schemas, and avoids guesswork on ambiguous prompts. By narrowing scope and enforcing domain boundaries, the system reduces hidden errors that would otherwise slip past databases and dashboards.

Rethinking evaluation: representative traffic and engineered ground truth

Evaluation begins with reality, not hypotheticals. Production traffic is analyzed to map common enterprise workflows and difficulty tiers, from basic aggregations to open-ended, cross-schema reasoning. That distribution guides a benchmark that mirrors how users actually ask and iterate on questions.

Ground truth is built from complementary sources. Academic suites like Spider and BIRD validate structural SQL skills. Internal experts contribute cases that reflect messy, real-world schemas and business logic. Third-party auditors run blind evaluations to supply thousands of unbiased query pairs. LLM-as-a-judge scales grading on complex prompts, but absolute scores are treated with caution because internal tests show a 10 to 20 percent optimistic bias on numeric tasks. The signal is most reliable for pairwise comparisons between model versions, which speeds optimization without overstating progress.

The corpus includes more than 2,000 golden queries for regression protection, thousands of intentionally ambiguous prompts that check whether Genie seeks clarification, and multi-turn dialogues that test context retention over longer analytic sessions.

From offline rigor to production signals and regression guards

Evaluation uses the exact production code path in a sandbox to eliminate test drift. High-coverage benchmarks run daily, and automated alerts route regressions to engineers before users feel them. Offline coverage is continuously refreshed with edge cases that customers flag in the wild.

Production signals close the loop. Strong engagement behaviors like data exports and shared reports correlate with trusted answers. Thumbs-up and thumbs-down feedback, error-category analysis, and anomaly detection on output formats help surface semantic drift and recurring failure modes. New patterns are folded back into the evaluation set, keeping tests aligned with evolving usage rather than static labs.

Managing stochastic models for deterministic analytics

LLMs are probabilistic, yet BI consumers expect the same correct answer to the same question every time. Genie manages variance with concrete few-shot examples, narrow semantic spaces, and consistent SQL patterns. Ambiguous prompts trigger clarifying questions instead of speculative answers. Reported gains are validated with strict confidence levels to separate real improvements from sampling noise, novelty effects, or temporal variance that can swing single measurements by about 10 percent.

Operational discipline turns GenAI into trustworthy BI

Trustworthy AI analytics comes from disciplined engineering around the model: governed access, representative benchmarks, daily regression guards, production feedback loops, and statistical rigor. Absorbing that complexity allows teams to stop second-guessing results and act with confidence on AI-accelerated insights.