• workshop_dense_100#slide 100

A sensible closing-slide recommendation is a next reading order that follows the internal logic of the programme rather than the mere numbering of topics. First, read 08-RAIDT_Foundations_M_V50 to secure the core object: the run as the unit of governance, the run-level evidence pack, the five pillars (Responsibility, Auditability, Interpretability, Dependability, Traceability), and the score profile with anchors 1=missing / 3=partial / 5=audit-ready. Second, read 12-RAIDT_DSR_Theory_M_v8 to understand why that object is framed as a mechanism-based mid-range design theory, how influence methods as governance interventions generate outcomes, and what the boundary conditions are. Third, read 11-RAIDT_Academic_Logic_M_v11 to see the wider programme consolidated across implementation, policy mapping, sector calibration, and empirical validation.

The decision path should mirror that reading order. Decide first whether the use case is a material run that warrants RAIDT treatment. Decide next what the minimum run-level evidence pack must contain. Then decide how the five pillars will be scored and calibrated. After that, decide which interventions will be tested or governed, such as structured prompting, retrieval augmentation, PEFT/LoRA, or preference-based alignment. Only then decide the implementation depth, the relevant policy and standards mappings, and whether the case should proceed into a wider pilot or empirical programme. The closing message is therefore practical: establish the evidence object first, then build the governance programme outward from it.

For a public-service team deploying GenAI for eligibility advice, the closing slide could direct readers to Foundations first so they understand why a single disputed advice run must be reconstructable. The next reading is the design-theory paper so they can see why retrieval augmentation or logging are not just technical options but governance interventions with boundary conditions. The final reading is the academic-logic paper because it shows how implementation, policy mapping, and empirical validation fit together.

The accompanying decision path is equally concrete: define the material run, specify the evidence fields, calibrate scoring, choose and log interventions, select the implementation model, and only then move to standards mapping and wider rollout.

• 08-RAIDT_Foundations_M_V50
• 11-RAIDT_Academic_Logic_M_v11
• 12-RAIDT_DSR_Theory_M_v8