PAG: A Formal Grammar for Structuring LLM Prompts (banes-lab.com)

Pattern Abstract Grammar (PAG) has been introduced as a structured instruction format for large language models (LLMs), aimed at reducing the interpretive ambiguity associated with natural language prompts. By employing explicit syntax patterns—such as the frequently encountered tokens READ, WRITE, IF, and FOR—PAG provides a clearer structure that aligns with how LLMs are trained. This structured approach replaces vague prose with unambiguous statements, allowing for more reliable interactions with AI systems and addressing the inherent ambiguity found in natural language. The significance of PAG lies in its ability to enhance consistency in model outputs by offering a formal grammar that ensures better parsing of inputs. It establishes a framework for structured instructions, including numbered phases and validation gates, honing in on precise tasks while acknowledging the probabilistic nature of LLM responses. While PAG doesn't execute commands or guarantee specific outputs, it reduces the interpretative burden on models, potentially leading to more deterministic behavior. By integrating structured programming concepts with understandable semantics, PAG may improve the communication between users and AI systems, making it a noteworthy development in the AI/ML community.