Concept·Ideation·Augmentation·Developing·CON-085
Design Decision Documentation
Value hypothesis
Product goes live with articulated design decisions explicit in institutional memory, rather than creating knowledge debt that will have to be reconciled at the next iteration.
Efficiency · Risk Reduction
To document their design choices, designers gather both raw inputs from on-the-job decisions (meeting notes, a Figma comment thread, a Slack discussion, a quick verbal rundown) and formal sources (Figma annotations, concept decks, demos, storyboards). The LLM compiles this into a formal decision record in a specified target format, including elements like user needs, alternatives considered, tradeoff rationale, and business or technical implications. The record is reviewed and approved by the designer, then integrated into the relevant documentary source-of-truth for future reference. In fast-moving teams, this helps scattered decisions become traceable and institutionally retrievable by other designers and engineers, and context consumable by other AI tools and workflows.
Risks in application
Black Box Rationale
A confident but wrong rationale is derived from thin source material: a Slack thread full of shorthand becomes a polished paragraph of coherent-sounding reasoning. Future readers treat the record as the authoritative account of why the decision was made, but the actual reasoning (contested, partial, politically shaped) gets overwritten by a smoother, model-produced story that lacks essential detail.
Shallow Solutions
A polished decision record produced from shorthand source material can mask the absence of actual reasoning: the rationale reads as deliberate when the underlying decision was never actually made. This creates false traceability, and a future liability when record considered by future teams turns out to be hollow.
Expertise that differentiates
Business Framing
The designer is the only person who can certify what the decision was actually about: why one option won, which constraint was binding, which stakeholder concern tilted choice. An LLM can structure a record but cannot reconstruct the weight placed on each factor; and a record without weighting reads as accurate but doesn't transmit necessary nuance.
AI Fluency that assures
Creation Diligence
Careful selection of the correct inputs to assure the decisions are captured from where they are explicit or, lacking explicit decisions, sufficient implicit context to allow reliable interpretation.
Deployment Diligence
Creating decision records regularly enough that the work doesn't get ahead of the rationale. This is especially important in semi-automated workflows and prompt-to-prototype concepting, where material can move very quickly without pausing to externalize the key choices that have been made.
Possible Indicators
Effort reduction
Time drafting and reviewing design decision records, versus baseline for manual authorship from equivalent raw source material.
Audit trail completeness
Proportion of design decisions in a project that have a published, retrievable record, versus decisions that are undocument