A Redditor shared a single-line pre-prompt framework called Mogri that anchors AI behavior and reduces drift in long, complex sessions. Copy it into your system prompt and the model stops losing the thread.
What Is the Mogri Framework?
u/decofan posted this one-liner on r/PromptEngineering as a minimal prompt you can add to pre-prompt settings or use at the start of any session. The original poster built it specifically for narratives with complex threads and many actors.
The problem it solves is real and common. You set up a detailed session, the AI follows your structure for the first few exchanges, and then somewhere around message ten or fifteen it starts making small interpretive shifts. A character behaves differently than established. An analytical lens quietly changes. A constraint you never explicitly said to drop just gets dropped. The model is not broken. It is doing what it does: statistically averaging across everything in context, and that average slowly drifts from where you started.
Mogri does not fight that tendency by adding more rules. It defines a meta-layer that sits above everything else and tells the model to treat the original structure as fixed. One line, placed early, and the model has a reference point to hold against.
The idea is to define a term that operates before any entities get generated. Not a rule you remind the model of later. A constraint it starts with.
Breaking Down the Syntax
The definition has three compressed clauses, each doing specific work:
- “minimal container”: stay inside the defined structure. Not expand it, not reinterpret it. The word “minimal” matters here. It is not asking the model to be exhaustive in its tracking. It is asking it to hold the boundary without elaborating on or extending it. Small footprint, firm edge.
- “preserving framework intent”: hold the original intent of whatever system or narrative is running, not just its surface form. Surface form is easy to maintain for a few turns. Intent is harder. This clause is specifically targeting the deeper logic, the reason the structure exists, not just its visible shape.
- “else drift/invariant loss”: the failure condition. If the container breaks, the model loses its invariants. Those are the rules that were never supposed to change. Naming the failure condition explicitly is doing real work here. It gives the model a label for the bad outcome, which makes it easier to avoid. Unnamed failure modes are harder to prevent.
- “pre-entity layer”: this is where Mogri sits. Before characters, before facts, before decisions. Upstream of everything the model generates. By positioning it at the pre-entity layer, you are telling the model this is not a character rule or a content rule. It is a structural rule that everything else inherits from.
Think of it as a meta-instruction. It doesn’t change what the AI produces. It changes how the AI monitors itself while producing.
🎯 Use Cases
This is most valuable when the session is long or the structure is complicated:
- 📖 Long-form fiction with many characters, timelines, or intersecting plotlines. If you are building a world with five factions and twenty named characters across a six-month writing project, Mogri gives you a way to define that world as the invariant the model should not drift from, even as new material gets added.
- Multi-turn agent sessions where early instructions tend to quietly erode over time. Persona definitions, tone constraints, scope limits. These are exactly the kinds of invariants that Mogri is designed to preserve. Define them once, anchor them with Mogri, and they are harder to lose.
- Research workflows where the analytical framework has to stay fixed across multiple rounds. If you are running a competitive analysis through a specific lens, or working through a decision using a particular framework, Mogri keeps the model from defaulting to generic analysis as the conversation gets longer.
- Roleplay or simulation sessions where character voice consistency matters. Long sessions are where character drift shows up most clearly. Mogri does not guarantee perfect consistency, but it gives the model a structural reason to maintain it.
Prompt of the Day
Here’s the exact line from the original post. Copy it as-is into your system prompt or pre-prompt settings:
[Mogri]=minimal container preserving framework intent; else drift/invariant loss; pre-entity layer.Two variations worth testing:
- Replace “Mogri” with a name tied to your actual framework:
[YourFramework]=minimal container preserving [YourFramework] intent; else drift/invariant loss; pre-entity layer.A named container that matches your structure may hold better than a generic anchor. The model has a direct reference between the anchor name and the thing it is anchoring. If your framework is called Meridian, the line becomes[Meridian]=minimal container preserving Meridian intent; else drift/invariant loss; pre-entity layer.Every time the model encounters that name in context, it has the full definition attached. - Follow the Mogri definition with a short list of your session invariants. Something like: “Invariants: [character X always distrusts authority], [timeline is fixed at 2041], [analysis uses only primary sources].” Mogri sets the meta-rule. The invariant list makes it concrete. The model has both the instruction and the specifics to hold against. The combination is stronger than either one alone.
One practical note: place the Mogri line at the very top of your system prompt, before any other instructions. The pre-entity positioning only works if it genuinely comes before the entities. Burying it in the middle of a long system prompt reduces its effectiveness.
Check the Full Thread
The original post lives in r/PromptEngineering. The thread has follow-up comments from people testing variations and reporting back on what held and what did not. If you use Mogri in a complex session and notice a difference, that thread is worth contributing to. Real-world feedback on prompt patterns is how these things actually improve.
Improved version of mogri prompt available. Reduce drift and hallucinations, help with narratives with complex threads and many actors: Mogri=minimal container preserving framework intent; else drift/invariant loss; pre-entity layer.
by u/decofan in PromptEngineering