Recent advancements in vector-based system prompts have significantly enhanced the response quality of open-weight large language models (LLMs) without the need for fine-tuning or external tools. By using lightweight YAML system prompts to set immutable values like compassion and truth, and allowing behavioral scalars such as curiosity and clarity to be adjustable, the study achieved notable improvements in response metrics. These include a 37.8% increase in response length, a 60% rise in positive sentiment, and a 66.7% boost in structured formatting. The approach, tested on the GPT-OSS-120B MXFP4 model, also resulted in a remarkable 1100% increase in self-reflective notes, all while maintaining factual accuracy and lexical diversity comparable to the baseline. This method simplifies earlier complex techniques into a portable scalar-vector approach, making it easily applicable across various LLMs like Gemma, Llama-3.3, and GPT-OSS. The research invites feedback on the practical implications of these enhancements, particularly in domains such as coding assistance and safety testing, and explores preferences for using YAML, JSON, or plain text for prompt injection. This matters because it demonstrates a scalable and accessible way to improve AI alignment and response quality using consumer-grade hardware.
The current approach to large language models (LLMs) often anthropomorphizes them, treating them like digital friends, which leads to misunderstandings and disappointment when they don't behave as expected. A more effective framework is to view LLMs as state machines, focusing on their engineering aspects rather than social simulation. This involves understanding the components such as the Substrate (the neural network), Anchor (the system prompt), and Peripherals (input/output systems) that work together to process information and execute commands. By adopting this modular and technical perspective, users can better manage and utilize LLMs as reliable tools rather than unpredictable companions. This matters because it shifts the focus from emotional interaction to practical application, enhancing the reliability and efficiency of LLMs in various tasks.