Indie Dev Postmortem: Stopping Your AI Data Agent from Lying with Real Numbers

Impressive_Wheel_877 · reddit · 2026-08-13

An indie developer shared lessons from building Alice, an AI growth agent for SaaS founders, focusing on the critical pain point of solving subtle hallucinations when LLMs process data.

The Problem

The model's most dangerous mistakes aren't fabrications, but misattributions—using real numbers in the wrong context (e.g., assigning Channel A's data to Channel B) or failing at simple mental math. These errors read as authoritative.

Core Engineering Practices

Deterministic Code on the Critical Path: If a rule must hold every time, enforce it in code, not in the prompt.
Verify Against a Fact Set: Code pre-computes all values. After the model writes, every number is matched back and scoped to its specific context.
Retry with Exact Violations: The retry prompt includes the offending sentence and the exact reason, keeping the version with fewer violations.
Code Decides, Model Writes: Code scores and picks the actual bottleneck; the model merely translates this verdict into plain English.
Don't Let It Do Math: All derived figures are computed server-side and handed to the model.
Test the Checker Harder: Aggressively test your validation logic to prevent false positives from destroying good output.
Provide a Playbook, Not a Blank Chat: Deliver the verdict and actionable next steps upfront, reducing the friction of users not knowing what to ask.

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Indie Dev Postmortem: Stopping Your AI Data Agent from Lying with Real Numbers

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