Users of AI systems typically see only the final Output that has been generated. Behind that Output, however, multiple stages of AI Processing and multiple Decisions may have occurred.

In the example shown, three DSEs are connected behind the final Presentation. DSE-001 records the Extract / Summarize process applied to the Meeting Minutes. DSE-002 records the Compare / Evaluate process using Internal Data and External Data, through which the Market Analysis is formed. DSE-003 then records the generation of the final Presentation based on the Content produced through the preceding stages.

From the user’s perspective, the result may appear to be a single Presentation. From a Governance perspective, however, multiple Decisions exist behind that Output, each supported by its own Evidence.

What matters is therefore not merely preserving the final Output, but preserving the relationships that show through which Content, Decisions, and Evidence that Output was formed.

This makes it possible to trace the origin of a conclusion if questions later arise about the Presentation. Through the chain of DSEs, it is possible to reconstruct which Content was used as Input, which comparisons and Evaluations were performed, which Decisions produced intermediate Content, and how that Content ultimately contributed to the final Output.

This differs from retaining only the final Output. When only the Output remains, its content can be examined, but it may no longer be possible to verify which Evidence and Decisions led to its formation.

DSE connects the final Output to Evidence of the process through which it was formed.

DSE does not separate the final Output from the Evidence of how it was formed.

Through this structure, an AI Output becomes more than a generated result. It becomes an Output whose formation can be traced backward and subsequently verified through the Decisions and Evidence that produced it.