System and Method for Supervised Autonomous Product Generation using Iteratively Optimized Prompt Sequences Derived from Structured Knowledge Graphs

A method for supervised autonomous product generation via a Language-Based Build System (LBBS), the method comprising:

• maintaining a structured specification defining high-level architectural intent in a documentation root of version-controlled natural language manifests;
• translating raw source materials into a technical architecture schema to decouple human-defined intent from system-managed implementation;
• executing an instruction set via a generative model to produce product output in a stateless environment according to a stateless dependency order;
• validating actions of the generative model against a dual-layered control architecture comprising a product-specific governance rubric and a global framework security rubric;
• monitoring the product output via a supervisor function to detect divergence from the documentation root;
• and
• performing a recursive update bifurcated into skill hardening for product-level fixes and skill accrual for systemic framework enhancements.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause a computing system to:

• manage a hybrid build architecture for supervised autonomous product generation;
• decouple high-level architectural intent from implementation boilerplate by mapping semantic concepts into a technical architecture schema;
• enforce a stateless dependency order during the generation of product output by a generative model;
• validate actions of the generative model against a task-locking governance rubric and a global security framework rubric;
• and
• initiate a recursive evolution cycle bifurcated into product-specific skill hardening and systemic skill accrual upon detection of a logic divergence.