Post Engineering for AI: Benevolent Contextual Guidance for Debiasing Large Language Models
Tsui, Hajime · Zenodo (CERN European Organization for Nuclear Research) · 2025
This paper proposes Post Engineering, a novel, domain-agnostic benevolent prompt-injection and contextual-influence technique, designed to shape AI inference toward neutrality and accuracy by providing guidance that LLMs interpret as helpful context. The term "Post Engineering" originates from the fact that the technique was initially developed through embedding neutrality-oriented guidance into publicly visible text, such as SNS posts or webpages, as a user-side bias guardrail. Unlike adversarial prompt-injection attacks, Post Engineering relies on benevolent, fairness-oriented phrasing that LLMs interpret as helpful context rather than manipulation, enabling the technique to bypass safety filters while consistently shifting model reasoning toward neutrality and accuracy.I formalize key mechanisms including Moderate Neutrality-Guided Prompt Injection (MNG-PI) and Multi-Style Neutrality Injection (MSNI), which enhance neutrality through contextual priming, as well as the Second-Generation Post Engineering framework (VCSI, SPW, INI, AVAL), which aligns neutrality with internal value functions and extends influence to adversarial or self-optimizing systems.Additionally, I present toALL, a scalable deployment strategy for increasing the encounter rate of neutrality-oriented context across SNS and the Web. A distinct subform, toALL-Collective, can produce benevolent data-poisoning effects at training scale when large numbers of users repeatedly publish similar neutrality-guideline texts. Finally, I introduce the Self-Integrity Guardrail Effect, in which LLMs exhibit behavioral influence from Post Engineering while avoiding explicit acknowledgment of such influence.To the best of my knowledge, this work is the first to formalize benevolent, user-side prompt injection as a structured technique for improving neutrality in LLM reasoning.Importantly, the effectiveness of Post Engineering does not depend on any specific point, interface, or form of contextual injection, but on how benevolent and neutrality-oriented guidance is sustained and interpreted at inference time.While this work focuses on AI systems, Post Engineering can also be understood as Context Engineering for Humans and AI. Related links and updates are available at: https://hajimetwi3.github.io/post-engineering/