Which data?
Period, instruments, frequency, provenance, quality status and, where relevant, licensing or usage conditions.
Documentation is not an appendix written at the end of the project. It is part of the system. For a material finding, a reviewer should be able to see five things: which data were used, which rules applied, which version was calculated, under which conditions the test ran, and what limits follow from those conditions.
A robust result requires more than the number itself: data state, calculation and decision rules, system version, test conditions and known limitations all matter. The documentation is intended to make that chain visible from the published finding back to its underlying basis.
Period, instruments, frequency, provenance, quality status and, where relevant, licensing or usage conditions.
Decision rules, parameters, filters, execution assumptions and risk logic must belong to a defined rule state.
A rule change creates a new state. Results from different versions should not be silently combined.
Test period, control group, comparison path, cost assumptions and other conditions determine what the number actually means.
Every finding should state what has not yet been tested – such as intraday ordering, costs, new data or isolated APS impact.
The evidence page, for example, states the scope of the Version 1 test matrix. That number alone would be of little value. The documentation chain gives it meaning: 94 real instruments were evaluated with 60 oscillators under the same basic logic, producing 5,640 systematically executed individual tests.
The context also states that these tests share market, data and formula families and therefore must not be treated as 5,640 independent scientific experiments. Those details prevent a correct number from producing an incorrect conclusion.
A historical comparison, a documented test and an independent replication are different evidence states. The site therefore uses visible status labels.
The statement can be traced in the current source or test base.
The number or observation exists, but is not presented as universal performance evidence.
The finding is intended to be reproduced or isolated under Version 2 conditions.
The function or test belongs to the target architecture but is not yet a finished component.
A future user or acquirer should not find contradictory system states in marketing material, source code and manuals. The long-term objective is a coherent chain of domain documentation, technical architecture, test records, user help and version history.
What the rules mean, why they exist and which market or risk situation they represent.
How modules, data models, interfaces, error handling and operating conditions are implemented.
Which version was run, when, on which data and whether the expected reference output was reproduced.
Contextual assistance for users – ultimately in German and English and ideally available inside the product.
Generative AI can search large knowledge bases, structure text, formulate explanations and assist quality assurance. Precisely because it can do so, it needs explicit sources, versions and approval rules. AI-generated wording is not a new domain standard until it has been checked against the documented system logic.
For every core claim it makes the metric, current support, methodological limitation and next validation step visible.
A number should not merely be readable. A reviewer should be able to see why it appears on the website and which test would change its status.
View Evidence Map →