Back with some papers. (Apologies in advance; I typically don't edit/format comments much here, please bear with me.)

Notable papers describing performance improvements with prescribed roles and personas:

- ExpertPrompting: Instructing Large Language Models to be Distinguished Experts (2023) https://arxiv.org/abs/2305.14688 (if you're going to only read one paper here, maybe read this one but know there has been a lot of follow up with more modern models.)

- Expert Personas Improve LLM Alignment but Damage Accuracy (2026) https://arxiv.org/abs/2603.18507

- When Does Persona Prompting Actually Help? (2026) https://arxiv.org/abs/2605.29420

- Unveiling Power on Combining Prompt Engineering Techniques: An Experimental Evaluation on Code Generation (2025) https://doi.org/10.5753/sbbd.2025.247251

- A Pattern Language for Persona-based Interactions with LLMs (2025) https://www.dre.vanderbilt.edu/~schmidt/PDF/Persona-Pattern-...

A TLDR of my *admittedly heavily biased* mental model (so take it with a grain of salt): personas do improve task alignment and precision to measurable effect but with observed negative impact to accuracy and knowledge grounding. Overall, this makes it quite suitable and preferred for code generation scenarios. (Don't over-index on 'accuracy' here as meaning "bad code", it's more about verbosity/jargon reducing clarity of higher order goals like business objectives and system architecture.)

Outside of code generation, personas have the interesting effect of increasing implicit biases and stereotypes. It's not hard to imagine something like "you are a left|right wing politician ..." or "you are a senior-citizen|teenager ..." influencing token space construction considerably.