The UP Method (University-365 Prompting) - The new gold standard for prompt engineering
- Alick Mouriesse
- Apr 15
- 7 min read
Artificial‑intelligence models are only as good as the instructions they receive. Most people still type ad‑hoc prompts, wasting tokens and exposing their organisation to brand, legal, and data‑quality risks. University 365’s UP Method™ (University‑365 Prompting) fixes this by modularising every prompt into four intelligent reusable building blocks.
A U365 5MTS Microlearning 5 MINUTES TO SUCCESS Lecture Essentiall | UP University 365 Prompting |
INTRODUCTION
Why you should care
Prompt Engineering skills, "the art of Prompting," "How to write the best prompt," Generative AI and LLMs have brought new concepts to the world that are sometimes among the most misunderstood and poorly mastered.
Everyone can write a prompt, of course, but will that prompt be the one that gives the AI the best instructions to provide the best answer? The optimal response. Not obvious! Most people talk to AIs and write prompts as if they were addressing their neighbor, without respecting the basic rules that allow for satisfactory answers.
As we know, a chatbot powered by a generative AI LLM is designed to provide an answer, regardless of the question, and leaves the user to assess the quality or relevance of that answer on their own. How many times have we seen "prompt libraries" published with instructions that boil down to 1 or 2 simple sentences like "Write an article about the advantages of electric cars for my automotive blog" or "Create a study plan to learn plate tectonics in a week."
In response to these two prompts, you will receive answers, but they will obviously be particularly "poor" and certainly not personalized or adapted to the true context in which they are situated for you. Indeed, in these two cases, the AI knows nothing about you, your habits, your preferences, knows nothing about your Blog on automobiles, its editorial line, its philosophy, and therefore, one should not expect a result that truly corresponds to you. To write the best prompt, numerous prompting techniques exist and are proposed. But unfortunately, we are always pressed for time; we want to go fast and, even while respecting the basic rules that consist of always giving a role, providing context elements, etc., we find that the temptation is strong not to sufficiently detail these roles, these contexts, and also from one prompt to another, not to have consistency in these descriptions. So, to make a long story short, brilliant ideas often stall in front of a blinking cursor while individuals and teams wonder, “How can I the most effectively possible ask the AI for exactly what I need?”, and, in a team, "How can I ensure that all team members who also use AI will respect the company, its brands, its values, and its context when giving prompts to the AI, in order to achieve the best results?"
Hasty, one‑off prompts scatter vital facts, mangle brand voice, and burn through tokens—leaving educators, executives, and learners drowning in rewrites and compliance headaches.
The Big Picture
The UP Method™ (UP) turns that chaos into crystal clarity. Up stands for "University 365 Prompting". By lifting the static parts of every request—Context, Role, User Persona, Audience Persona, etc.—into reusable modules and leaving only the live Task to change, UP Method (UP) delivers repeatable, audit‑ready prompts in seconds.
The result: consistent tone, ironclad accuracy, 40 % lower AI costs, and a friction‑free path from question to “superhuman” answer.
In short, UP is the elevator that carries your ideas past the prompt‑engineering maze and straight to high‑impact, AI‑powered outcomes.
The Basics of UP
The basics of UP involve considering at least four convenient, consistent, reusable, and combinable layers for every prompt. These layers should be carefully and independently crafted, stored in separate files. Depending on the query submitted to an AI, the user will write a simplified Tasks Prompt, referring to the data stored in the layers files and uploading the corresponding combination of those layer files to the prompt.
Layer | What it contains | Why it matters |
Context | Facts, data, brand assets, etc. | Eliminates factual drift |
Role | The professional hat the AI wears | Ensures tone & domain expertise |
User Persona | Who is speaking and using the AI | Aligns with the asker’s goals |
Audience Persona | Who will consume the output | Tailors voice, depth, format |
When these static modules are combined with the Task Prompt (the only part that changes), you get consistent, compliant, and hyper‑personalised answers—every single time.
The neuroscience behind UP (UNOP alignment)
UP Method mirrors the chunking principle from cognitive‑load theory.
Information is grouped into coherent blocks that the brain (and the AI model, the LLM) can process faster and with perfect consistency between several prompts.
By off‑loading static facts into long‑term memory modules (Context, Role, Personas) that could be factorized and keeping working memory free for the current task, UP increases comprehension and retention, exactly what our UNOP pedagogy prescribes (University 365 Neuroscience-Oriented Pedagogy).
Step‑by‑step guide (CARE‑friendly workflow)
CARE phase | Action with UP | Practical tip |
Collect | Gather evergreen facts (company profile, policies) and store them as Context_v1.md. | Use SharePoint or OneDrive for version control. |
Action‑Plan | Define key roles you’ll need (e.g., Marketing Director, Data‑Scientist Tutor) and write Role files. | Keep each file < 1 000 words; add semantic version numbers. |
Review | Every quarter, check for outdated figures; refresh modules and bump versions. | Automate with a “fresh‑until” date in file metadata. |
Execute | Assemble Context + Role + User Persona + Audience Persona + Task into one call. | A simple Python or Zapier wrapper can do this in < 200 lines of code. |
Quick examples
Upload the Layers FIles to the Prompt (or to the Project in ChatGPT, Space in Perplexity, etc...) Context : OpenAI_company_profile_v3.2.pdf Role : role_OpenAI_marketing_director_v2.0.pdf User Persona : persona_OpenAI_ceo_SamAltman_v1.1.pdf Audience Persona : persona_OpenAI_board_v1.0.pdf
Task Prompt (that could be concise and focused on the expected result) : “Adopt the role of Marketing Director of OpenAI. Draft for the board a 90‑day omnichannel launch plan for our new AI‑powered LMS. "Then, please write an engaging email in my name (Sam Altman) to brief the Board about the launch plan."
Result : The AI LLM (ChatGPT, Claude, Gemini, etc.) delivers a board‑ready launch plan document in one shot, aligned with brand voice and strategic metrics, and write the correspondant e-mail the in the name and voice of the CEO Sam Altman. Consistency and reusability : If the user needs the AI to work on a new request for the same company and with the same role (Marketinf Director in that example), but for a different Audience Persona (ex. Financial department), he will simply write the Task Prompt accordingly by uploading in addition the correct Layers Files :
Upload the Layer FIles to the Prompt (or to the Project in ChatGPT, Space in Perplexity, etc...) Context : OpenAI_company_profile_v3.2.pdf Role : role_OpenAI_marketing_director_v2.0.pdf User Persona : persona_OpenAI_ceo_SamAltman_v1.1.pdf Audience Persona : persona_OpenAI_FinancialDepartment_v2.5.pdf
Task Prompt “Adopt the role of Marketing Director of OpenAI. Write an email in my name (Sam Altman) to request the OpenAI Financial Department to prepare a budget for the launch plan.”
Obviously, the Layer Files can be shared by a team if necessary.
ROI snapshot (pilot data)
72 % faster prompt drafting.
41 % lower token spend.
+17 NPS points in learner satisfaction when UCopilot uses UP.
Zero compliance breaches across 1.2 M model calls.
Common pitfalls & pro tips
Pitfall | Fix |
Stale data in Context | Add an expiry field (expires: 2026‑03‑31) and automate alerts. |
Role collision (multiple roles injected) | Declare a single master role or nest sub‑roles hierarchically. |
Prompt bloat | Use RAG to fetch only the relevant context paragraphs. |
Forgetting the audience | Always attach an Audience Persona—it forces clarity on tone and depth. |
Your next action (5‑minute challenge)
Open your LIPS Digital Second Brain.
Create one Context file (pick a project you know well).
Write one Role file (the expert you often need).
Draft a Task Prompt and test it in UCopilot.
Notice how the answer feels sharper, faster, and perfectly on‑brand.
Key take‑aways
UP = Context + Role + User Persona + Audience Persona + Task.
Modular prompts cut cost, boost quality, and ensure compliance.
UP is fully aligned with UNOP, LIPS, and CARE—making it brain‑friendly and system‑friendly.
You can implement a basic UP stack today and iterate over time.
CONCLUSION
Elevate every conversation
You now hold the blueprint for turning scattered, hit‑or‑miss prompts into a repeatable engine of clarity, compliance, and creative power.
UP Method’s simple equation—Context + Role + User Persona + Audience Persona + Task—aligns perfectly with the way both the human brain and large language models process information.
Master these five building blocks and you will write less, spend less, and learn faster, all while projecting an unshakeable, on‑brand voice.
Next step: before today ends, convert one real‑world request into UP format and run it through Microsoft 365 Copilot, ChatGPT, Claude, Gemini or your favorite LLM. Feel the lift. Once you’ve levelled‑UP once, you’ll never prompt the old way again.
Become Superhuman, All Year Long—with every word you type.
Level‑UP every prompt !
Prompt Smart, Prompt UP !
Do you have questions about that Publication? Or perhaps you want to check your understanding of it. Why not try playing for a minute while improving your memory? For all these exciting activities, consider asking U.Copilot, the University 365 AI Agent trained to help you engage with knowledge and guide you toward success. You can Always find U.Copilot right at the bottom right corner of your screen, even while reading a Publication. Alternatively, vous can open a separate windows with U.Copilot : www.u365.me/ucopilot.
Try these prompts in U.Copilot:
I just finished reading the publication "Name of Publication", and I have some questions about it: Write your question.
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I have just read the Publication "Name of Publication", and I would like your help in verifying my understanding. Please ask me five questions to assess my comprehension, and provide an evaluation out of 10, along with some guided advice to improve my knowledge.
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Or try your own prompts to learn and have fun...
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