---
id: "action-audit-signal-fidelity"
type: "action-item"
source_timestamps: ["00:13:00", "00:13:35"]
tags: ["data-quality", "systems-architecture"]
related: ["concept-signal-fidelity", "framework-world-model-principles"]
action: "Assess whether your World Model is being fed high-fidelity operational telemetry or low-fidelity chat and document logs."
outcome: "Establishes a realistic ceiling for the model's accuracy and dictates how much human oversight is required."
sources: ["s15-block-layoffs"]
sourceVaultSlug: "s15-block-layoffs"
originDay: 15
---
# Audit the Fidelity of Your Input Signals

## Action

Assess whether your [[concept-world-model]] is being fed high-fidelity operational telemetry or low-fidelity chat and document logs.

## Outcome

Establishes a realistic ceiling for the model's accuracy and dictates how much human oversight is required.

## How To Do It

Before trusting a World Model, you must audit the ground-truth quality of the data feeding it.

### High-Fidelity Signals
- Operational telemetry
- Financial transactions (the [[concept-signal-fidelity]] case)
- Sensor data
- Structured event logs from production systems

### Low-Fidelity Signals
- Slack messages
- Google Docs
- Email threads
- Meeting transcripts

## What To Conclude From the Audit

If your model is primarily built on text-based communication, recognize that the context graph is *slippery* and highly prone to misinterpretation. If the inputs do not provide a clear, factual fingerprint of your business, you must invest in clarifying those inputs before expecting the model to provide reliable insights.

This audit determines:

- The realistic accuracy ceiling
- How aggressively to mark the [[concept-interpretive-boundary]]
- Whether [[concept-semantic-retrieval]], [[concept-structured-ontology]], or [[concept-signal-fidelity]] is the appropriate architecture (see [[framework-world-model-architectures]])

## Related

- [[concept-signal-fidelity]]
- [[framework-world-model-principles]]
- [[framework-world-model-architectures]]