Social policy discussions are where AI conversations go sideways fast. If the model drifts mid-session, defaults to simplistic framing, or inherits decades of biased messaging, you end up with shallow analysis dressed as insight. Redditor u/decofan shared a three-part prompt setup on r/ChatGPTPromptGenius designed to address all three problems before the conversation even starts.
The framework is built around one principle: GIGO. Garbage in, garbage out. If the inputs are distorted by biased assumptions or sloppy framing, no amount of clever questioning will fix the output. Most people try to fix this mid-conversation by adding more context or rephrasing their questions. That approach rarely works because the damage is already done. So fix the environment first.
What This Prompt Is Solving
The author identifies three specific failure modes in AI conversations about social policy:
- Context drift: long, complex policy sessions cause the model to lose its original framing over time. You start with a nuanced setup and ten exchanges later the model is responding as if it never happened. The longer the session, the worse this gets.
- Purity metaphor abuse: framing issues as “clean vs dirty” creates a mental shortcut that blocks real analysis, and the model picks it up fast. Once that framing takes hold, it shapes everything from word choice to what solutions get treated as viable.
- Drug-war propaganda bias: decades of biased public messaging have shaped how we all think about these topics, and that bias quietly seeps into the prompts we write. This one is the sneakiest because you don’t notice it. It’s baked into the vocabulary, the assumptions about causation, and which outcomes we treat as obvious.
The fix is to address each one explicitly, before the real conversation begins.
The Three-Part Preload
Each component in the setup tackles one of those failure modes:
- 🔧 Mogri: a foundational cognitive framework (linked on GitHub in the original post) that acts as a conceptual container for the session, preventing the model from drifting as complexity builds. Think of it like setting ground rules for how the model should reason, not just what it should say.
- Purity suppression: a specific instruction that stops the model from defaulting to “clean vs contaminated” framing, which the author calls “a very costly shortcut.” This matters because purity framing quietly narrows what counts as a valid solution. It turns policy questions into moral questions, and that’s a different conversation entirely.
- Propaganda correction: a prompt that counteracts the cognitive warping from decades of drug-war-era messaging, so the model can approach the topic with fewer inherited distortions. This isn’t about politics. It’s about accuracy. Messaging that repeated for 40+ years shapes what feels like common sense, even to people who intellectually reject it.
Think of this less as a single prompt and more as a setup phase. You run it before your actual policy discussion, not during it. The goal is to create a session environment where the analysis can actually be useful, rather than just confidently wrong.
Who This Is Actually For
This is a niche setup with a specific audience in mind. If any of these apply to you, it’s worth testing:
- Policy researchers and advocates working on drug reform or harm reduction
- Journalists covering social policy who use AI for background research
- Educators teaching critical thinking on politically charged topics
- Anyone who’s noticed AI responses on policy topics feel frustratingly surface-level or slanted
It’s also useful for anyone building long research sessions around complex social topics where session coherence matters. If you’ve ever had a model that seemed to “forget” its own framing by message 20, this addresses that directly.
📋 Prompt of the Day
Here’s the full setup from u/decofan, reproduced exactly as shared:
GIGO
Preload your model with:
[Mogri] foundational cognitive/conceptual pre-everything container
Suppression of [purity] metaphor abuse
[Correction] of the warping effects of living under decades of drug-war propaganda
The bracketed terms each link to GitHub resources shared in the original post. Each one is a standalone document you load into context before starting. Head to the Reddit thread to grab those links directly and review the full documentation behind each component.
Why the Framing Matters
The real insight here is that you’re not prompting for answers. You’re prompting for a better thinking environment first. The author’s framing is sharp: “we don’t know any different, a chatbot can help us find the different.”
That’s a genuine use case most people haven’t explored. The usual approach is to trust that the model is neutral and just ask better questions. This setup rejects that assumption entirely. It treats the model’s training environment as something to work around, not rely on. You’re asking the model to acknowledge and work around the biases baked into how we all discuss these topics, before touching the actual substance.
Whether it performs exactly as described depends on your setup and how you build from it, but the underlying logic is sound. Bias in, bias out. Address the bias first, and you have a better shot at getting analysis that’s actually worth acting on.
Check out the original Reddit post for the GitHub links and to see how the community is building on this framework.
A prompt for working with social policy issues
by u/decofan in ChatGPTPromptGenius