Absolutely, AI can write with real information, but the crux lies in mastering how to use AI to perform this task.

For me, navigating this landscape has been a substantial journey, learning to engage with AI effectively to extract meaningful value. This process involves not only leveraging AI's capabilities to enhance outcomes but also understanding the potential and mechanics of these interactions. Over the past year and a half, my focus has been on honing skills with ChatGPT, as opposed to other AI tools, driven by a desire for depth rather than breadth.

Table of Contents

• Absolutely, AI can write with real information, bu …

• If you want to learn how with examples - Continue …

  • As per your request: A weekly Prompt

  • Now let’s learn, how to incorporate real data effe …

  • So what's next?

  • Why do I give you a Huge Prompt Example each week? …

• Wrapping it all Up!

  • If you have a podcast, I would love to speak on it …

  • If you have a newsletter or website, I would love …

  • Your Feedback Matters -

Mastering this tool to the extent that it genuinely serves my needs is an exhilarating achievement. So, to revisit the initial question: Can AI write with real information?

Unquestionably, yes. However, the pertinent follow-up question is how to accomplish this effectively. While it's known that tools like ChatGPT operate on a vast database of information, my emphasis on writing with 'real information' extends beyond mere database retrieval.

It's about integrating concrete data into the AI tool, prompting it to construct case studies or content grounded in the information provided, supplemented by its knowledge base of factual data to ensure authenticity in the output.

This includes, for instance, dictating into Google Docs and allowing the AI to draft content, which I can later refine. The goal is to employ AI as a writing companion, enhancing the content with genuine information, and thus, maximizing its utility and relevance.

Here is the Cole's notes Main Take-away for this newsletter....

I value your input so please "Reply to this email" and share your thoughts or opinion! What topics do you want to see covered and what is working/not working with your AI and case writing journey.

If you want to learn how with examples - Continue reading or See you Next Time.

As per your request: A weekly Prompt

Now let’s learn, how to incorporate real data effectively.

To achieve this with AI, you must understand the art of prompting, especially for crafting case studies. For starters, got to teachingwithcases.com and utilizing the free generator simplifies this process. It will create a focused and excellent 1000+ word prompt that will get you an awesome case to start.

The generated prompt sets the stage for the case's topic, and subsequent additions should consist of real information intended for the case study. This process necessitates thorough review to ensure alignment with the intended message.

So how do we write with real data?

The process could look something like this…..

• Step 1: Use my Free Prompt Generator OR paste in AI your own prompt to generate a Case Study BUT at the end of the prompt, make sure to add in the details and data that you want the case study to be about. For example, if you wanted to do a case study on the governance problems that OpenAI had with Sam Ultman being fired back in November of 2023, you would make sure to add in factual data into the prompt and instruct the AI to generate the case BASED ON THE FACTUAL DATA PROVIDED. Making sure to not add in and false or non-factual data. (I over simplified this but you get the gist)

• Step 2: Read the case and make additional prompts to craft the content to fix what you are intending to create for your case

• Step 3: Prompt the AI to add in any additional factual data that you supply and make sure that is integrates that data seamlessly and completely into the completed case.

• Step 4: Read the final case and make adjustments.

This directive helps maintain the case study's authenticity, leveraging the AI’s vast database solely for supportive context, not creative embellishment.

********Clarifying instructions to the AI is paramount; it operates on prediction and inference, so detailed prompts are crucial to eliminate ambiguity. The length and specificity of the prompt don’t just aim for better outcomes; they provide clear, concise direction, enabling the AI to deliver more accurate and relevant results promptly.

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So what's next?

Here is an example prompt from my free Case Study Prompt Generator.....

Case Category: Journalism & Media Studies
Case Topic: Media Wars - Paper vs Digital Media
Case Style: General Information Case

You are a expert case study writer who will take this information and write a complete case based on this information with the category and topic. Main Prompt for Case Generation:

To use a language model like GPT-4 to create case studies, you need to follow a series of steps that involve understanding the case, defining its scope, preparing the data, and finally generating the case studies. Here is a detailed breakdown of the process:
1. Understand the Case: Firstly, understand what kind of case study you want to generate. This might include the topic Media Wars - Paper vs Digital Media, the intended audience, the structure of the case study, and the kinds of information you want it to include. These factors will guide your preparation of the data.
>>>>The writing length MUST be detailed, reflective of the content and build upon itself so that the final product is detailed.
1. Generate a Main Theme is Journalism & Media Studies.
2. The topic is Media Wars - Paper vs Digital Media.
3. Type of case: General Information Case.

Data for establishing kinds of cases Case studies can be used in various fields to analyze and understand different aspects of a particular subject or situation. They can be categorized in different ways based on the purpose they serve, the approach they take, or the structure they follow.

Here are a few categories of case studies based on the structure or approach:
1. Exploratory Case Studies: These are condensed case studies performed before implementing a large-scale investigation. The primary goal is to help identify questions and select the types of measurement prior to the main investigation.
2. Descriptive Case Studies: These involve starting with a descriptive theory. The subjects are then observed and the information gathered is compared to the pre-existing theory.
3. Explanatory Case Studies: These are often used to do causal investigations. In other words, researchers use these to study how event A leads to event B. These structural categories allow you to decide on the kind of case studies you will be generating. If you are focusing on AI-generated case studies, you might want to keep the structure more streamlined and simple. Descriptive or illustrative case studies might be best suited for AI generation because of their straightforward structure.

>>>>The structure of the case is... When considering your target audience, think about who will be most interested in the topics you are covering in your case studies. This could be businesses looking for insights into specific situations, students learning about particular subjects, or professionals seeking knowledge or understanding of specific phenomena. The type of case studies you decide to produce can definitely affect your target audience. For instance, exploratory case studies might be more useful for academic or research audiences who are planning in-depth investigations. On the other hand, descriptive or illustrative case studies might be more suited for a more general audience looking for a comprehensive understanding of a particular subject.

Your answers to these questions will help define the kind of data you need to train your model.
1. Case study scope: The industry can be anything, the focus or subject for the topic is your discretion and the issue is yours to decide; this is a business case.
2. Data Preparation: Once you have a clear understanding of the type of case studies you want to generate, you'll need to collect and prepare the data to train the model. You might consider the following steps:
3. Data Formatting: In order for the model to understand your data, you need to format it correctly. This means deciding on a consistent structure for your case studies and making sure all of your data follows this structure. I need some figures and numbers that can add to the case story; you generate them and make them flow.
4. Generating Case Studies: Once the model is fine-tuned, you can generate case studies. You can do this by providing the model with a prompt and asking it to generate the rest of the text. The prompt might include some details about the case study you want, and the model will generate text based on what it learned during fine-tuning.
5. Generate a university-level Case Study that is detailed and consistent in the theme of Journalism & Media Studies and the topic is Media Wars - Paper vs Digital Media. The writing needs to be complete, straightforward, and not biased towards any one player in the case. The topic is Media Wars - Paper vs Digital Media, and have enough detail in the scenario that students have enough time to read the case (3 pages in length) and then take their knowledge and apply that critical thinking to the case questions that we will create after. Here is some information on why people use cases...

:::::::Why are case studies used? A case allows (the student) to step figuratively into the position of a particular decision-maker. Cases allow students to put themselves in the place of actual leaders and decision-makers. Students are able to analyze situations, develop alternatives, choose plans of action and implementation, and communicate and defend their findings in small groups and in class. Cases are used to test the understanding of theory, to connect theory with application, and to develop theoretical insight. Cases are one of the best ways to enable students to learn by doing. The Case study needs to follow the Business Case Study Format provided.

:::::::Business Case Study Format A typical case study begins with a paragraph describing the problem facing a manager, leader, or executive. The case often gives background information on the industry, the organization, and the stakeholders involved. Much of the background information for a case is in the public domain. The case then typically returns to the dilemma faced by the protagonist. At this point, it is helpful to include comments from company executives. Their perspective, management style, and deliberations bring realism to the case. The case concludes with a recap of the problem addressed at the outset of the case. Depending on the focus of the case, it may include financial statements, organization charts, process flow charts, and other related information.

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Why do I give you a Huge Prompt Example each week? 

I hope that is gives you some ideas as to how to prompt AI to get the most out of it for Case Study Writing.

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Wrapping it all Up!

If you are interested in trying out the Free Case Study Prompt Generator .. Click the image below.

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Thank you for being a part of our community. Together, we're shaping the future of education, one case study at a time!

Warm regards,

Matthew 

The Teaching with Cases Team