generative design and dynamo blog by weeklydynamo Newsletter Source

Why useful AI work needs roles, result gates, and governed memory - especially for AEC/BIM automation. 0. Opening - Why One Assistant Was Not Enough Most AI workflows still begin with a single assistant. Ask a question. Get an answer. Copy the result somewhere else. Start again next time. For simple tasks, this is enough. If I need a short summary, a draft email, a quick explanation, or a small code snippet, one assistant can be extremely useful. But the moment the work becomes continuous, the single-assistant model starts to break. In my case, the work does not live inside one prompt. It crosses multiple layers: AEC automation projects Revit and Dynamo workflows Python scripts Generative Design experiments AI model training notes research papers blog articles YouTube and LinkedIn content presentation decks project-specific standards client-facing deliverables long-term knowledge management A single assistant can answer well in the moment. But it usually does not...

Generative design is often described as a way for software or AI to create design options automatically. In practice, the more important point is not automation itself. The real value comes from defining design variables, evaluation criteria, and a search strategy so that many alternatives can be generated, compared, and improved. For AEC workflows, this matters because design decisions are rarely based on a single criterion. A building option may need to balance area efficiency, daylight, cost, constructability, code constraints, structural logic, and documentation requirements. Generative design tools help explore that decision space, but each tool is built around a different workflow and level of control. The Basic Structure of Generative Design Most generative design workflows can be understood through three components: a generator, an evaluator, and a solver. Component Role Generator Creates design alternatives from input variables and constraints. Evaluator Measur...