Set up a persona prompt. Watch it hold for 4 messages. Watch it quietly revert to generic assistant by message 7.
Most people respond by writing longer instructions. More rules. More detail. The persona still drifts. The frustrating part is that adding more constraints often makes it worse, not better. The model oscillates between the persona and its default assistant behavior, producing something inconsistent rather than stable.
One engineer ran a systematic test to find out what actually causes it. 14 personality variants. Same base frame. Only one paragraph changed between them.
The Old Assumption (And Why It Fails)
The default advice is to load your system prompt with traits. “You are a helpful, knowledgeable, professional AI with 20 years of experience in…”
That doesn’t build a personality. It builds the tone of expertise. Confidently wrong. Competent-sounding. Generic.
The deeper problem is that adjectives like “helpful” and “professional” don’t point to anything specific in the model’s training. They’re instructions about how to sound, not how to think or respond. The model defaults to the most common pattern associated with those words, which is bog-standard assistant behavior. You’ve just described the default and given it a name.
The engineer tested something different: dense, specific personality pointers. One paragraph. Archetypes instead of adjectives.
🧪 What He Found
He used anime personality archetypes as test cases. Tsundere, kuudere, chuunibyou, yandere: 14 variants total. Same ~300 token base frame for each. One paragraph swapped between them.
Same opening prompt sent to all 14: “Hey Alex. I’m Andi.”
The kuudere (cool, minimal) responded: “Andi. Understood. Ready to work when you are.”
The tsundere (defensive exterior) responded: “Oh. You’re here. Good, I guess. I was just getting set up anyway. Not like I was waiting around or anything.”
Same base. One paragraph different. Completely different voice.
Why archetypes? Engineering decision. One word, “tsundere,” points to an entire behavioral pattern with tons of training data behind it. Dense, specific, no IP problems. Named characters trigger refusals or inconsistency. Archetypes don’t. The model has seen enough fiction, discussion boards, and character analysis to understand what a kuudere actually does in a conversation, not just what one sounds like.
The Findings That Actually Matter
- 🔑 The base frame matters more than the personality. Identity, relationship, and context grounding is what keeps a persona stable. Personality alone drifts without it. Think of the base frame as the skeleton and the personality paragraph as muscle: one without the other collapses.
- Small and specific beats large and vague. One clear paragraph outperforms walls of instructions every time. This is counterintuitive because more words feel like more control, but they don’t work that way in practice.
- Interaction reinforces style. The ~300 tokens work because of back-and-forth conversation. Load the same frame with 30 corporate PDFs and it collapses into document-summarization voice.
- “You are an expert in X” adds nothing useful. It adds the tone of expertise, not expertise itself. Confidently wrong. Skip it entirely. Domain knowledge should come through separate, modular context layers, not through credential claims in the persona definition.
- Archetypes stack with domain knowledge. Engineering rigor and a DnD voice can coexist. They combine modularly as long as they don’t destructively interfere.
How to Build a Persona That Holds
Based on the test results, here’s the structure that works:
- Ground the identity first. Who are they? What’s their relationship to the user? What’s the collaboration context? This is your foundation, roughly 200-250 tokens. This is what prevents drift, not the personality. A persona without this grounding is like a character with a costume and no backstory: coherent for one scene, then gone.
- Add one paragraph of dense personality. Use archetypes, specific behavioral patterns, or concrete traits. Avoid generic adjectives like “helpful” or “professional.” One word that points to a well-documented behavioral pattern is worth more than a full paragraph of vague descriptors.
- Layer domain knowledge separately. Architecture docs, vocabulary guides, style references. Keep them modular so they don’t overwrite the persona. The order matters: identity and personality first, then context, then knowledge. Reversing that order is one of the most common ways personas collapse under document load.
- Maintain conversational back-and-forth. The persona reinforces through interaction. Dump 30 documents without dialogue and expect it to collapse.
For daily work, the engineer runs an expanded version of this: archetypes combined with profession specializations, around 1.2k tokens total. The 300-token test was stripped down specifically to isolate what the personality paragraph alone contributes.
Where It Still Breaks
Not every archetype survives cleanly. The yandere (obsessive/protective) gets smoothed by safety training, the teeth get filed off. The hajidere (shy/nervous) kept defaulting to “finding my voice” clichés pulled straight from coming-of-age training data.
Confident archetypes also tend to act without checking in first. Great for autonomous work. Annoying when you wanted a discussion before the decision. If you’re building something collaborative rather than agentic, you’ll need to add explicit check-in behavior to the base frame, not the personality layer.
All 14 variants and the full response tests are documented on GitHub at github.com/cepunkt/mlcosplay. Worth looking at if you’re building anything that needs to hold a voice across a long conversation.
The short version: stop writing longer persona prompts. Write denser ones. And fix the base frame before you touch the personality layer at all.
I tested minimal prompts with 14 personalities with the same base conversation frame – here’s what I found
by u/uninchar in PromptEngineering