# Traceability

Traceability is the ability to connect an AI result or action back to its source data, instructions, tools, and approvals.

## Why it matters

Traceability lets a team connect an output to the model version, instructions, retrieved evidence, tools, approvals, and downstream actions that produced it. This supports debugging, governance, and correction.

## Example

A generated quarterly metric links back to the exact warehouse query, source timestamp, calculation step, report version, and employee approval.

## FAQ

### What is Traceability in simple terms?

Traceability is the ability to connect an AI result or action back to its source data, instructions, tools, and approvals.

### Why does Traceability matter for teams using AI?

Traceability lets a team connect an output to the model version, instructions, retrieved evidence, tools, approvals, and downstream actions that produced it. This supports debugging, governance, and correction.

### What is a practical example of Traceability?

A generated quarterly metric links back to the exact warehouse query, source timestamp, calculation step, report version, and employee approval.


Source: https://www.luffy.so/ai-glossary/traceability

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