Prompting 101 - 00/10 Introduction - The Basics
- Martin Swartz
- Feb 13
- 13 min read
Updated: May 17
With this series of 10 microlearning lectures for beginners (plus this introduction), you will learn essential prompt engineering techniques to create clear and precise prompts and master any Large Language Model (LLM) Chatbot like Open AI ChatGPT, Microsoft Copilot, Anthropic Claude, Google Gemini, Deepseek, Alibaba Qwen, X AI Grok, and many others.
A U365 5MTS Microlearning 5 MINUTES TO SUCCESS Lecture Essential | 📖 Series Introduction - Basics |
Prompting 101
A U365 Series of 10 Microlearning Lectures
INTRODUCTION
For centuries, humans have refined how we present information, from ancient oracle queries to modern-day search engine optimization. Prompt Engineering stands on the shoulders of these traditions, harnessing technology to bridge gaps in understanding. As we look to the future, the art of writing effective prompts will shape how we collaborate with intelligent tools.
Welcome to "Prompting 101" an introductory Series of 10 microlearning Lectures designed to enhance your ability to engage with large language models (LLMs) and chatbots effectively.
Many people have interacted with LLMs like Chat GPT, Gemini, Claude, Le Chat, or Copilot without fully understanding how they work. This often leads to questions that result in vague or inaccurate answers.
This series is ideal for beginners looking to unlock AI's full potential.After reading that Series of 10 micro lectures + Introduction, we recommend you to read the protocol of the University 365 Prompting (UP) Method : The UP Method gives you very pratical tips and tricks to skyrocket your results with game-changing habits when using your favorite LLM Chatbot with pratical exemples for OpenAI Chat GPT, Anthropic Claude, Google Gemini, etc.Prompt Engineering is the process of creating clear, structured instructions to help AI systems produce accurate, relevant responses. It’s more than just typing words into a text box—it's about crafting context, guiding the AI’s reasoning, and managing outcomes.
While LLMs in general, and especially are designed to respond to virtually any prompt, the quality of their responses varies significantly based on how well the prompt is constructed. This is where the power of effective prompting comes into play.
Large language models, such as the ones behind popular chatbots, utilize advanced algorithms and vast datasets to generate human-like text responses. Their popularity has surged due to their ability to provide instant information, assist with creative writing, and enhance productivity across a range of applications. However, success in leveraging these powerful tools depends on the art of the prompt—the specific way you ask your questions.
In this microlearning Introduction Lecture, you'll quickly and almost immediatly discover how to think strategically about the questions you pose to LLM chatbots. By mastering the principles of effective prompting, you'll learn how to create clear and structured instructions that guide the AI toward generating accurate and relevant responses. This course will empower you to recognize the qualities of well-formed prompts and equip you with techniques to craft them efficiently, making your interactions with LLMs not only more productive but also more enjoyable.
LLM AI chatbots are designed to provide answers to any question. However, the accuracy and quality of the responses depend on the clarity and precision of your question (the prompt). Poor prompts always result in poor responses.
Prompt Engineering is not just about typing words into a text box; it’s about extending clarity, context, and intention into your queries. Just as our methods of communication have evolved, Prompt Engineering captures the essence of these advancements. As we move forward, mastering the art and science of writing effective prompts will become essential in navigating the future of human-AI collaboration.
Join us in this journey to unlock the full potential of your interactions with language models. Let's transform the way you ask questions and receive answers, enhancing both the accuracy of responses and the overall experience.
Prompting 101 Series
Introduction + 10 Microlerning Lectures
PLAN
From this Introduction Lecture and even before discovering the 10 other "Microlearning Lectures" in this series, we promise you that you will never "Prompt" like before!
Welcome to University 365 Prompting 101 Series
U365'S VALUE STATEMENT
At U365, we understand that beginners need clarity and actionable guidance when stepping into new territory. That’s why we focus on delivering a practical foundation for building effective prompts. By the end of this microlearning lecture, you’ll see how structured, well-thought-out instructions can dramatically improve AI-generated results.
OVERVIEW (Key Takeaways)
Clarity Over Complexity – Simple, direct language gives better results.
Context Is Critical – Provide background or examples to guide output.
Consistent Format – A consistent prompt structure helps maintain reliable outcomes.
Iterative Improvement – Start basic, test, then refine.
Adaptability – Customize prompts to fit different scenarios or tasks.
LECTURE ESSENTIAL
Understanding AI, LLMs, and Chatbots: A Simple Breakdown
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, enabling them to think, learn, and perform tasks that typically require human cognition.
To simplify, we will say that within AI, there are different types:
Traditional AI
Traditional AI relies on Rule-Based Systems (RBS), Machine Learning (ML) and Deep Learning (DL) to process and analyze data.
• A Rule-Based System (RBS) is a type of Traditional AI that operates using a set of predefined IF-THEN rules to make decisions or solve problems. These systems do not learn from experience like Machine Learning (ML) models; instead, they follow explicit instructions programmed by humans.
Examples of Rule-Based Systems :
Spam Filters – If an email contains words like "WIN MONEY" or "FREE PRIZE," it is flagged as spam.
Early Chatbots – If a customer asks, "What are your business hours?" THEN the bot responds with predefined store hours.
Expert Systems (e.g., Medical Diagnosis) – If a patient has a fever + cough, THEN suggest "possible flu" and recommend a doctor visit.
Fraud Detection – If a credit card is used in two different countries within an hour, THEN flag it as potential fraud.
Automated Loan Approval – If income > $50,000 AND credit score > 700, THEN approve the loan.
• Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables systems to learn from data and improve their performance over time without being explicitly programmed. It is widely used in various industries to automate decision-making, predict trends, and enhance user experiences.
Here are some real-world applications of Machine Learning (ML):
Business & Finance
Fraud Detection – Banks use ML to detect suspicious transactions based on patterns (e.g., unusual spending locations).
Credit Scoring & Loan Approval – ML analyzes customer data to assess creditworthiness and automate loan approvals.
Stock Market Predictions – ML models analyze historical stock data to predict price movements and trends.
Customer Segmentation – Businesses use ML to group customers based on behavior, allowing personalized marketing.
Media & Entertainment
Recommendation Systems – Platforms like Netflix, Spotify, and YouTube suggest content based on user preferences.
Content Moderation – Social media platforms use ML to detect and remove inappropriate content (e.g., hate speech, spam).
Music & Video Generation – AI-powered tools create new songs or enhance video editing (e.g., AI upscaling old movies).
• Deep Learning (DL) is a further subset of ML that uses complex neural networks to analyze patterns in massive datasets, enabling more advanced decision-making.
Unlike Generative AI (like ChatGPT), which creates new content, Traditional AI is more structured and focused on solving specific problems using existing data.
Generative AI - AI based on Large Language Models (LLMs) and including DL and ML technologies
Large Language Models (LLMs) are advanced AI systems that use more and more ML and DL, and that are trained on vast amounts of text data to understand and generate human-like language. These models form the backbone of AI-powered conversations and content generation.
What Are Chatbots?
Chatbots, such as ChatGPT, Gemini, Copilot, Claude, Le Chat, Perplexity, and many others you have probably heard of, are applications built using AI and LLMs. They are not by themselve the LLM nor they represent, alone, what we called AI that is a much vest and complex set of technologies. Chatbots leverage natural language processing (NLP) to interact with users in a conversational way, answering questions, assisting with tasks, and simulating human-like dialogue.
By understanding these core concepts—AI, ML, DL, LLMs, and chatbots with NLP—you have a clearer grasp of how modern AI technologies function and impact our daily lives.
Now that we are in this AI universe, what about prompting and the so-called "Prompt Engineering" skill?
Understanding Prompt Engineering
Prompt Engineering refers to the intentional crafting of prompts to direct an AI model effectively. Think of it like giving precise instructions to a virtual assistant or a teammate. The more accurate and detailed your request, the better the outcome.
To start doing things the right way and to get straight to the point, you should know that a good prompt includes at least four essential qualities:
ROLE that you want the AI to play when answering the question: What role do you want the AI to fulfill, and what specific knowledge and behaviors you're expecting from the AI.
CONTEXT of your question to the AI: Background information about who is asking (it can be you a the person you're asking for), what the person who's asking is doing, and for what purpose that person is asking to the AI. Provide the tone, scope of your question, and the target audience you are addressing, if applicable.
PERSONA that you represent in the context of your question, and if applicable, the PERSONA represented by the audience for whom the question is asked.
GOAL you want the AI to reach with the answer: Now, it's time to explain the outcomes you wish to achieve with this AI's assistance. Do not forget to mention the targer audience
CLARIFY your question by specifying what you expect or do not expect: Remember that clarity is essential. Always write in a way that minimizes ambiguity by using a clear structure. For that reason, you can specify your expectations more clearly, as well as clarify what you do not expect.
The Importance of Clarity
One of the most common pitfalls made by the majority of people talking to an Large Language Model is vague or incomplete prompts. For example, imagine you want to undestrand what is photosynthesis.
AVOID VAGUE PROMPTS AT ALL COST
Look at the differences between a vague promt and a better one respecting the Goal, Context, Clarity framework:
Vague Prompt: “Explain photosynthesis.” With that prompt, the AI chatbot will definitely provide an answer. That answer may seem acceptable, leading everyone to say, "Wow, it's done! We asked a question, and we've got an answer that seems okay." However, it’s important to note that while there is often an answer when you ask an AI chatbot, the quality and accuracy of that answer may fall short of your expectations.
Better Prompt, respecting the Role, Context, Persona, Goal, with Clarity “ You are an expert in biology, specialized in plants. You are especially pedagogical and know how to explain things clearly and precisely, adapting to any audience. You will explain a specific topic about biology to me as if you are my personal teacher and coach. I am a 15-year-old student interested in how plants work, especially in photosynthesis. I need to learn everything about photosynthesis so I can give a 30-minute presentation and discuss the topic with my class next week. I need you to explain the concept of photosynthesis, focusing on why it is important for young generations to understand it better. Then, help me write my talk's script.” By precisely specifying the Role (Expert in biology), the Context (personal teacher), The personas (15 years old student targetting his class in a college), the Goal (explain ) with audience and focus and clarity, you guide the AI to produce a more targeted, precise, accurate response.
Please use the AI LLM chatbot of your choice andTRY IT ! Test these two prompts to see the differences in the results. You'll be amazed!
Now you undestand why it's so important to craft the right prompt. And you will never again rush to Chat GPT to ask it a question, stupidly, as if you were asking a search engine!
For even better results, we invite you to craft your prompts using the UP Method (University 365 Prompting). This approach encourages you to create separate, reusable personal CONTEXT, PERSONA, and ROLE files for each situation. You can "convoque" these files when appropriate by uploading them in the prompt or within the project. Discover the UP Method.
IMPROVE YOUR PROMPT By Building Prompt With Even More Structure, Provinding Format, Constraints, Tone & Style
When writing prompts respecting the Role-Context-Goal-Clarity framework, you can also consider adding these elements:
FORMAT & LENGHT: Describe how you want the AI gives you the desired result or output format. If you need a certain format (like bullet points or a short summary), include that explicitly. ALso mention if needeed the lenght of the answer in terms of paragrphs, sentences, words or even caracters.
CONSTRAINTS: Indicate additional settings, give examples, or limitations so the AI aligns its response.
TONE & STYLEl: State if you need the answer to be formal, casual, or somewhere in between.
CONTINUE IMPROVEMENTS WITH ITERATIONS
You have to understand that Prompt Engineering is also an iterative process. That means you can begin with a first draft of your prompt, test it, see the results, and note any shortcomings. Then refine it, and summit again to the AI-LLM-Chatbot to see the differences in the answer:
Add details or constraints to reduce ambiguity.
Specify the desired length or format.
Offer examples of correct answers or templates for the AI to model its response.
Don't hesitate to test diferent versions of your prompt to choose the best answer.
Beware of Common Pitfalls
Overloading the Prompt: Too much detail can confuse the AI, leading to wandering answers. Avoid absolutely giving contradictory information or instructions.
Forgetting the Role or The Audience: If the AI doesn’t know who it’s supposed to be or the reading level and background knowledge of the audience for whom the answer is intended, the response may miss the mark.
Ignoring Feedback: Each AI output provides clues on how to adjust your next prompt for improvement. Use that to improve the next prompt or question.
PRACTICAL APPLICATION
Scenario 1: Summaries
Lets try with some real-world examples. Imagine you need a quick summary of a recent research article. Lets prepare the right prompt with this structure:
Goal: Summarize key findings in plain language.
Context: The article is about new battery technology for electric cars.
Building the appropriate prompt with Clarity results in the following prompt example (assuming you will provide the targeted research article by attaching it or copy-pasting it after your prompt): “Provide a one-paragraph summary of the main findings in that new battery technology study, focusing on cost savings and extended range.”
Scenario 2: Email Draft
You want a polished email to your project manager:
Goal: A concise update on project progress.
Context: The team is behind schedule but has made breakthroughs in process efficiency.
Prompt Example: “Draft a professional email to my project manager explaining that we are behind schedule but have improved efficiency by 20%.”
Step-by-Step Implementation
Define Your Purpose: Begin with what you want (summary, outline, idea generation).
Identify Key Points: Include target audience, tone, any specialized vocabulary.
Test the Prompt: Ask yourself if the instructions are clear and complete.
Analyze the Results: Does it answer fully? Is it too detailed or too vague?
Refine: Adjust the wording, length, or constraints to address deficiencies.
HOW-TO
Begin with a Draft
Write a simple one-sentence request that includes what you want the AI to do.
Add Necessary Context
Identify 2-3 pieces of critical information the AI must know (topic details, audience, tone).
Clarify Constraints
Is there a word limit? A time frame? State these clearly to control the output length and focus.
Check for Ambiguity
Read your prompt and imagine you’re brand new to the topic. Is anything missing or unclear?
Refine and Test
Provide your prompt, review the AI’s response, then update the prompt if needed.
INTERACTIVE REFLEXIONS
Reflection Questions
How does defining your goal upfront change the quality of the AI’s output?
Which part of your prompt requires the most attention or tweaking?
Quick Practice Exercise
Write a prompt to generate a short product description for a new smartphone having specific features. Add at least two constraints and review the response for relevance and clarity.
Mini-Project
Develop a step-by-step prompt to instruct an AI to compare two competing product lines (e.g., sneakers or laptops). Ensure you specify style, audience, and format.
CONCLUSION
Prompt Engineering is both an art and a science: a fusion of clarity, context, and iteration. By mastering the basics of crafting structured instructions, you'll unlock an AI's potential to deliver insightful, targeted outputs. By the end of this introduction, you will have nearly all the fundamentals needed to significantly enhance your prompts and, accordingly, the results you’ll receive from any AI LLM Chatbot.
Next Lecture Title: “Prompting 101 - 01/10 -Mastering the Art of Prompt Tuning”
Respect the UNOP Method and the Pomodoro Technique Don't forget to have a Pause before jumping to the next Lecture of the Series. Use UNOP Sound Track - 5mn Pause if needed. |
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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