Trust Isn't About Prose—It's About Provable Numbers

The Stakes: Trust Can Shatter on a Single Number

While building data agents, a single wrong number can shatter customer trust. That's why reducing LLM “hallucinations” isn't a nice-to-have—it's existential.

OpenAI's latest piece highlights a root cause: most scoreboards reward confident guesses over calibrated uncertainty. If leaderboards only prize “accuracy,” models learn that a wrong-but-certain answer beats “I don't know.”

What This Means for Builders

• Redesign evals. Track accuracy, abstention, and error as separate first-class metrics; weigh errors highest and abstentions lowest. Then optimize to the new weighted objective—not a flat “accuracy.”

• Reward calibration. Prefer models (and prompts) that express uncertainty when evidence is thin. Treat this as a product choice, not just a model choice.

• Ground aggressively. Use retrieval, tools, and post-hoc self-checks—but measure them with the reweighted metrics above so you don't optimize for confident guesses.

The Aliveo AI Approach

At Aliveo AI, we go a step beyond: the most complex analyses are run using audited internal code, while the LLM only orchestrates via external, controlled functions. The model doesn't “freehand” critical math; it calls the right tool. The problem isn't eliminated, but it's drastically reduced—bounded, testable, and traceable.

Trust in Data Agents isn't about perfect prose—it's about provable numbers.

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Our Go-To Strategies to Reduce Hallucinations

1. Three-Metric Evals (A/A/E):* Report Accuracy, Abstention rate, and Error rate separately. Optimize with error-weighted loss so one catastrophic miss outweighs many easy wins.
2. Selective Answering & Thresholding: Calibrate confidence scores; refuse or defer when below threshold. Tie UI behavior (e.g., gray states, tooltips) to these thresholds.
3. Evidence-First Generation: Force chain-of-custody: retrieval → cite → reason → answer. Penalize answers with missing or mismatched citations.
4. Tool-Calling by Default: Route math, stats, and joins to audited functions (SQL, Python, notebooks). LLM provides orchestration only.
5. Schema- and Unit-Tested Outputs: Use structured outputs (JSON/SQL) with validators (type checks, ranges, invariants). Add unit tests for critical transforms.
6. Dual-Model or Verifier Pass: Pair a generator with a critic/verifier (rule-based or model-based) to catch contradictions, unit errors, and unsupported claims.
7. Self-Consistency & Cross-Checks: Sample multiple reasoning paths and vote; require agreement on key numbers, or escalate to tools/humans.
8. Deterministic Math & Idempotent Plans: Cache intermediate results; prefer deterministic pipelines so the same question yields the same number.
9. Guardrails on Prompts: Constrain with instructions that prioritize uncertainty expression and ban numeric fabrication (“state unknown or fetch”).
10. Adversarial Evals & Red Teaming: Attack with OOD phrasing, misleading tables, and near-duplicate entities; measure degradation across A/A*/E.
11. Live Monitoring & Incident Review: Track post-deploy error rate and abstentions; maintain an error registry with fixes and retrofitted tests.
12. Human-in-the-Loop for High Stakes: Gate P0/P1 workflows behind reviewer approval when confidence is low or impact is high.

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A Practical Starting Point

• Define your weights: e.g., Error = 10, Accuracy = 1, Abstain = 0.2.
• Add a verifier: Start with simple numeric/range checks; expand to model-based critics.
• Move math to tools: Push aggregations, joins, and stats to audited code paths today.
• Show your work: Always surface sources, steps, and checks to users.

When the scoreboard changes, the behavior changes. Make uncertainty a feature, tool calls the norm, and numbers provable.