Decision-trace completeness
How much of the path from goal to decision can actually be reconstructed.
Formal definition
Decision-trace completeness. A canonical LLMography term. See the formal definition in the article below.
Formal definition
Decision-trace completeness is the proportion of a decision path that can be reconstructed from available evidence, including residual gaps.
In plain language
Decision-trace completeness is not a dashboard widget. It is a way of naming a part of Human–AI work that otherwise disappears into logs, chat history, or an unexamined final answer.
Why it matters
As systems move from prompts to agents and loops, decisions are produced across many steps. Without this concept, those steps remain difficult to reconstruct, compare, or audit.
Diagram
The signature object of LLMography is a trajectory: human, model, tool, observation, verification, revision, decision. Decision-trace completeness occupies a distinct position on that object.
Related metrics
See the proposed measures for loop autonomy, verification strength, and decision-trace completeness. These are research indicators, not certified scores.
Example
A recommendation is issued after a tool call fails verification and a human requests a narrower claim. Reconstructing that path is an instance of this concept in use.
Citation
Bousmah, M. LLMography concept note: Decision-trace completeness. LLMography research site.