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. This series is ideal for beginners looking to unlock AI's full potential.
A U365 5MTS Microlearning 5 MINUTES TO SUCCESS Lecture Essential | 📖 Series Introduction - Basics |
Prompting 101
A U365 Series of 10 Microlearning Lectures
INTRODUCTION
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.
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.
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.
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
Prompting 101 - 01/10 - Mastering the Art of Prompt Tuning
Prompting 101 - 02/10 - Advanced Constraints and Contextual Frames
Prompting 101 - 03/10 - Dynamic Prompt Architectures
Prompting 101 - 04/10 - Iterative Prompt Refinement Techniques
Prompting 101 - 05/10 - Harnessing Systematic Bias Control
Prompting 101 - 06/10 - Prompt Validation and Testing
Prompting 101 - 07/10 - Industry-Specific Prompt Adaptations
Prompting 101 - 08/10 - Measuring Prompt Impact and Efficiency
Prompting 101 - 09/10 - Prompt Security and Ethics
Prompting 101 - 10/10 - Innovations and Future Trends
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's "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: What role do you want the AI to fulfill, and what specific knowledge and behaviors you're expecting.
Context: Background information about who you are, what you are doing and for what. Provide the tone, or scope.
Goal : Now, it's time to explain the outcomes you wish to achieve with this AI's assistance.
Clarity: Remember that clarity is essential. Always write in a way that minimizes ambiguity by using a clear structure.
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.
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, Contexte, Goal, Clarity: “ You are an expert in biology specialized in plants. You are particularly pedagogical and know how to explain things with precision and clarity, adapting to any audience. I am interested in the functioning of plants, particularly in photosynthesis. I want to know everything about it so that I can discuss it at my next conference on plant biodiversity in my son's college. I need you to explain the concept of photosynthesis to a beginner biology class, focusing on why it is relevant for youg generation to better undestand thos concepts.” By precisely specifying the Role, the Goal with audience and focus, you guide the AI to produce a more targeted, precise, accurate response.
Please use the AI LLM chatbot of your choice and 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!
Building Prompt With Even More Structure
When writing prompts respecting the Role-Context-Goal-Clarity, you can also consider adding these elements:
Format: 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.
Constraints: Indicate additional settings, give examples, or limitations so the AI aligns its response.
Tone & Style: State if you need the answer to be formal, casual, or somewhere in between.
Iterative Refinement
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 for the AI to model its response.
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 Audience: If the AI doesn’t know the reading level or background knowledge, the response may be off-target.
Ignoring Feedback: Each AI output provides clues on how to adjust your next prompt for improvement.
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. |
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.
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