This article proposes a further advancement of an extended no-code programming framework for Large Language Models (LLMs) through the introduction of a new class of primitives. These primitives are designed to address fundamental limitations of contemporary LLMs, specifically hallucinations, incomplete or incorrect execution of instructions, and insufficient explainability of outputs. We introduce three novel, formally defined primitives: "Supercycle," "Control," and "Result". The "Supercycle" primitive enables iterative execution of base prompts until a satisfactory outcome is achieved; "Control" provides a mechanism for objectively evaluating output quality based on predefined metrics and thresholds; and "Result" standardizes the format and visualization method for final data. Formal definitions, syntax, semantics, and usage examples are provided. To demonstrate the framework.s practical value, we conduct an in-depth analysis of the official European Parliamentary Research Service (EPRS) report on the EU AI Act, which identified 6 out of 8 key lobbying indicators.specifically, the exemption for open-source models, an excessively high FLOPs threshold (10^25), ambiguous risk criteria, and delayed implementation timelines favoring established players. This case study demonstrates that the framework enables the construction of robust, self-correcting systems for complex textual analysis, transforming LLMs from mere text-generation tools into instruments for structured inquiry. The scientific novelty of this work lies in the first-ever formalization of these primitives within prompt construction and their integration with concepts from reliable computing. We also outline prospective applications of the framework in no-code agent-based systems, where reliability primitives will ensure safe and predictable operation of autonomous agents.

Keywords

no-code programming, prompt engineering, Supercycle, Control, Legal document analysis