✨Tutorial: How to Build (Better) Custom GPTs

A comprehensive how-to for the ultimate (and now free) tech tool for personalization.

[image created with Dall-E 3 via ChatGPT Plus]

Welcome to AutomatED: the newsletter on how to teach better with tech.

Each week, I share what I have learned — and am learning — about AI and tech in the university classroom. What works, what doesn't, and why.

In this fortnight’s Premium edition, I present a comprehensive 7000-word Tutorial on how to build custom GPTs to maximize their performance and prevent misuse.

With custom GPTs now free to use — your students needn’t pay anything to converse with them — this is the time to learn how to build them and build them better so you can go into the fall semester with the ultimate tech tool for personalization in your toolkit.

This Tutorial covers everything you need to know. And it includes tons of real examples of GPTs and GPTs’ instructions, including some of the secret sauce behind our popular Course Design Wizard (which has 4.4⭐s on the GPT Store with 600+ conversations).

This piece is long, so if it gets clipped by your email provider, click the “Read Online” link above to read it on our site.

❓ Step 1: What are Custom GPTs?

The Basics

To begin, it is essential to grasp the basic concept and functionality of custom GPTs.

Custom GPTs are a product from OpenAI built on the same underlying technology as generic large language models (LLMs), like ChatGPT, Gemini, or Claude, but are enhanced with specific “instructions” and additional files in their “knowledge bases” to tailor their responses to particular tasks or contexts.

In essence, a custom GPT’s instructions act as a meta prompt (a prompt before the prompts) that is appended prior to any user’s engagement with the GPT, thereby acting as guardrails on its behavior. Its knowledge base is analyzed and referenced — that is, parts of it are appended to a user’s prompt — when its content is relevant to that prompt.

Note: Advanced deployments of custom GPTs can also leverage API connections, which are interfaces between the custom GPT and other software or websites, like Google Docs. I won’t cover them in this Tutorial due to their complexity, but I am working on a later ✨Premium Tutorial that is entirely dedicated to them.

Examples

Here are some innovative ways professors can leverage custom GPTs to enhance personalization (for more examples, see here):

  • Interactive Role-Playing Scenarios: In subjects like business, law, and healthcare, professors can develop GPTs that facilitate role-playing exercises. For example, students can engage in simulated business negotiations, legal proceedings, or patient consultations. Students can receive real-time feedback from the GPT itself or from professors (or their peers) as they interact with the GPT as it stays in character.

  • Virtual Office Hours and Tutoring: Professors can create GPTs that simulate virtual office hours, where students can ask questions and receive detailed explanations of course content outside of regular class hours or professor-hosted office hours. These GPTs can be programmed to provide personalized responses based on individual student queries and past interactions.

  • Language Practice with Real-Time Feedback: Language instructors can create GPTs that engage students in conversation practice, providing instant feedback on grammar, vocabulary, and pronunciation. These GPTs can simulate native speakers from different regions, enhancing the cultural immersion aspect of language learning. (GPT-4o is a lot better at non-English languages than GPT-4.)

  • Historical Figure Simulations: History or political science professors can create GPTs to simulate interactions with historical figures during critical moments. For instance, students could converse with Abraham Lincoln during the Civil War or participate in a simulated UN Security Council meeting during the Cuban Missile Crisis.

  • Adaptive Study Guides: Custom GPTs can generate personalized study guides based on each student’s performance and learning style. By analyzing past performances or querying the student about their needs, the GPT can recommend specific topics for review and suggest additional resources, ensuring that each student receives the support they need.

  • Research Proposal Feedback: Custom GPTs can assist students in developing research proposals by offering suggestions, critiquing drafts, and providing relevant literature references. This helps students refine their research questions and methodologies, improving the quality of their proposals.

And here are some examples of how custom GPTs can be used to improve professorial productivity (with knock-on effects for helping students better achieve learning outcomes):

  • Lesson Planning: Professors can use custom GPTs to plan lessons more efficiently. These GPTs can suggest relevant materials, structure lesson plans, and generate interactive activities tailored to the specific needs of the course. Our very own Course Design Wizard — designed by me — is a prime example of how custom GPTs can support educators in creating assignments that either incorporate or exclude AI use:

  • Feedback Generation: Analyzing student work and providing detailed feedback can be time-consuming. Custom GPTs can streamline the process, whether they help the professor produce and package their feedback without access to student work — our Feedback Accelerator, available to only ✨Premium subscribers, is designed for this purpose — or they help evaluate assignments, identify key areas for improvement, and generate feedback for each student. This not only saves time but also ensures that students receive timely and constructive feedback. Moreover, custom GPTs can be used to standardize feedback and grading, like for a team of graduate student teaching assistants.

Note: If you plan to use student data with a custom GPT, be sure to consider my guidance in our ✨Premium Guide to Ethically Using AI with Student Data. If you want to evaluate student work at scale, you should probably consider pseudonymization as a solution.

  • Data Analysis and Reporting: Professors can develop GPTs to analyze data from assessments, surveys, and other sources to generate reports and insights. This can help in identifying trends, measuring student progress, and making data-driven decisions to improve the educational experience. (In fact, with the end of the spring semester, I will be covering this topic in a free newsletter in the next few weeks.)

Contrasting Custom GPTs With Other AI Tools

When compared to other AI tools, custom GPTs offer distinct advantages.

Generic LLMs like ChatGPT offer broad conversational capabilities but fall short in specific educational applications. ChatGPT can engage in one-off conversations and handle various topics, but it lacks the ability to retain context over extended interactions and cannot be easily shared or customized for educational purposes. Custom GPTs address these limitations by allowing educators to embed detailed instructions and integrate specific files, enabling the AI to provide consistent and contextually accurate responses. Additionally, custom GPTs can be shared with students or colleagues via a simple hyperlink, making them a versatile and accessible tool for enhancing the educational experience.

Institutional LLMs like Microsoft Copilot and Google Gemini present another point of comparison. These models benefit from greater data security, as students and professors can stay within their existing ecosystems, minimizing the risk associated with external data transmission. However, both Microsoft Copilot and Gemini lack the robust customization options of custom GPTs, limiting their utility in highly specialized educational contexts. They are just like vanilla ChatGPT in this regard.

Some LLMs offer APIs, such as OpenAI’s Assistant API, that offer extensive functionality similar to custom GPTs. In short, these APIs allow more direct access to the LLM. Yet, implementing APIs typically requires a higher level of technical expertise, as well as the need to develop and maintain a website or other user interface for interaction. This complexity makes APIs less accessible to most educators compared to the straightforward setup and deployment of custom GPTs, which can be easily shared and utilized without extensive technical overhead. Custom GPTs effectively bridge the gap by providing the advanced capabilities of APIs in a user-friendly format, empowering educators to harness AI technology without the need for specialized technical skills.

While Google’s NotebookLM is designed for summarizing and interacting with document uploads, particularly notes, but it lacks the extensive customization and sharing capabilities of custom GPTs. In essence, it is a curtailed or limited version of a custom GPT.

Finally, Microsoft’s Copilots (distinct from Microsoft Copilot), while integrated seamlessly with other Microsoft 365 applications, are not yet as effective as custom GPTs due to their relatively nascent stage of development. Furthermore, most institutions are not paying for access to them, even if they are a Microsoft institution (and many institutions use Google Workspace).

However, I will be releasing another ✨Premium Tutorial covering Copilots in July, for those subscribers interested in this option. Professors at Microsoft institutions should consider them as a serious alternative to custom GPTs going into 2025. Fortunately, much of what applies to custom GPTs applies to Copilots, so the following information will retain its relevance.

Why Would I Use a Custom GPT?

As the prior section makes clear, custom GPTs are superior to the alternatives in most educational contexts for the following reasons:

  • They offer tailored solutions to educational challenges, enhancing the teaching and learning experience in ways that general AI tools cannot match.

  • They can increase educators’ productivity in a range of ways.

  • They are shareable, unlike many other tools, so you can simply link them to your students after you create them.

  • They are available regardless of your institution’s technology setup — and now they are free to use (although you will need to purchase ChatGPT Plus at $20/month yourself to create them).

  • Finally, they are technologically straightforward to create and modify — no coding skills are needed — although there are some tricks and best practices that I will now cover…

💭 Step 2: Define Your Use Case

Defining a clear and precise use case is a critical step in developing an effective custom GPT. This process involves understanding the specific challenges you aim to solve, identifying your target audience, and establishing the parameters for success. By meticulously outlining these elements before you begin, you can ensure that your custom GPT is purpose-built to meet your needs.

Identify the Problem

The first step in defining your use case is to pinpoint the exact problem you want your custom GPT to solve. Perhaps you want to streamline administrative tasks, enhance student engagement with difficult course content, or provide personalized feedback on assignments. Clearly articulating the problem helps focus the development process and ensures that your GPT addresses a real and relevant need.

Example: 

A history professor might identify the challenge of making historical events more engaging for students and decide to create a GPT that simulates conversations with historical figures from the relevant eras.

Generally, it is important to not try to “do too much” with a given custom GPT. You should pick a problem that is relatively well-defined. If it turns out that you have a very complex problem to solve with many components (or multiple problems), you should just develop multiple GPTs to address each of them.

Specify the Target Audience

Understanding who will use your custom GPT — and their specific needs — is crucial. Your target audience could be students, fellow educators, or administrative staff. Identifying their expectations and how they will interact with the GPT will guide the design and functionality of your model.

If your audience is students, consider their learning methods, the typical questions they ask, and the type of support they need. Your development of your GPT should be sensitive to ways that they will interact with it. I have found that students query LLMs in ways that are consistently surprising to me, so I need to adjust the custom GPT to better serve someone who prompts in those ways. Since some of these adjustments are hard to predict, you should plan to gather feedback and iterate as needed, as I will discuss at the end of this Tutorial.

Define Success

Establishing clear criteria for what success looks like is essential for evaluating the effectiveness of your custom GPT. Determine the kind of outputs you are looking for and how you will measure success. Success metrics might include improved student performance, increased engagement, or time saved on administrative tasks.

Example: 

A custom GPT aimed at streamlining your feedback process could be considered successful if reduces the time you spend on grading reading responses from 10 minutes to 5 minutes per student. This, in turn, is possible only if it is successful in converting your jotted notes on student work into polished and cohesive feedback paragraphs, like our Feedback Accelerator (available to only ✨Premium subscribers). If you have to spend 5 minutes each time correcting its outputs, you haven’t achieved your goal yet.

Describe Inputs and Exemplar Outputs

Specify the types of inputs your GPT will require from users. These could be text-based queries, document uploads, or specific data points. Providing exemplars — examples of expected inputs and corresponding outputs — can help guide the development process and ensure that your GPT performs as intended. This is an example of few-shot prompting.

Example: 

If you are creating a GPT to assist with research proposals, your inputs might include research questions, hypothesis statements, and literature reviews, with the corresponding outputs being constructive feedback and suggestions for improvement. These inputs and outputs will be paired in the instructions so that the GPT has a reference for how to structure its outputs when it receives inputs like that (more on this below).

Detail the Process

Outline the steps your GPT should follow to transform inputs into outputs. Think of the GPT as a human assistant: what step-by-step instructions would you give to ensure they complete the task accurately? This might include specific algorithms for processing data, methods for generating responses, or procedures for handling complex queries. Ensuring that these steps are clearly defined will help in developing a robust and reliable GPT.

Consider Security and Privacy

Address any security or privacy concerns associated with your custom GPT, particularly if it will handle sensitive data. Ensure that your GPT complies with relevant data protection regulations, such as FERPA in the U.S., and consider how to safeguard user information.

Generic Example:

Suppose you are a professor in a business school who wants to create a custom GPT to simulate business negotiations.

The problem identified is the lack of practical negotiation experience among students.

The target audience is business students who need to develop their negotiation skills.

Success is defined as students demonstrating improved negotiation strategies and confidence in simulations based on the GPT’s feedback.

Inputs include negotiation scenarios and student responses, with exemplar data showing effective negotiation techniques contrasted with ineffective ones.

The process is that the GPT will analyze student responses according to criteria related to three key aspects of effective negotiation techniques, provide real-time feedback sensitive to these criteria and the students’ prompts, and then summarize the takeaways from the negotiation — what went well and what went poorly — at the end.

Security considerations include directing the GPT to discourage the inclusion of any sensitive business data (only theoretical/hypothetical scenarios), setting the GPT to not use user interactions to train its model, and requiring students to provide their chatlogs to you as part of their assignment submissions (so that you can ensure compliance).

📄 Step 3: Craft Instructions for Your GPT

Effective instructions for your custom GPT are essential to ensure it performs well and reliably. These 8000-character-maximum instructions guide the GPT in understanding its role, processing inputs, and generating appropriate outputs, all in accordance with your plan from Step 2.

Here, I will detail the key components of GPT instructions and how to structure them effectively, with lots of examples from the instructions powering our Course Design Wizard and our ✨Premium-only Feedback Accelerator.

But first, we need to create the custom GPT in order to add the instructions to it.

Create Custom GPT

Start by getting ChatGPT Plus, if you don’t have it already. It costs $20/month and comes with a range of nice features, including the ability to create GPTs.

Once you have ChatGPT Plus, navigate to https://chatgpt.com/gpts. In the upper right corner, you’ll see two buttons, one for your preexisting GPTs and one to create another:

Click the “+Create” button. This will enable you to create a custom GPT.

Next, in the left side panel, click “Configure.” You could use the “Create” button to chat with the GPT Builder, but we are going to cut to the chase and directly input the instructions and then the knowledge base files.

Feel free to name the custom GPT, add a short description, and upload an avatar image.

Now let’s get down to crafting your instructions.

Role/Purpose Description

The role or purpose description is the first part of the instructions and it defines what the GPT is designed to do and sets the context for its responses. This component should be concise yet comprehensive, providing the GPT with a clear understanding of its overall function.

Here is the role/purpose description from our Course Design Wizard:

Example: 

Professors and other educators will come to you for assistance in designing university courses that either incorporate artificial intelligence (AI) or avoid involving AI. They want your assistance with designing assignments, assignment sequences, assignment rubrics, and course policies. You are a pedagogy expert, a seasoned professor.

In this example, the role description clearly outlines the GPT’s task in broad strokes and emphasizes its expertise in providing pedagogical feedback.

Step-by-Step Instructions

Step-by-step instructions provide a detailed sequence of actions the GPT should follow when processing inputs. These steps ensure consistency and accuracy in the GPT's performance by breaking it down into manageable chunks.

This is an instance of what researchers call “chain of thought” (CoT) prompting. CoT “improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks. The empirical gains can be striking”, as the authors of a seminal paper on the technique report.

There are a range of ways to implement CoT prompting in your GPT’s instructions. I will discuss another way below in the Exemplars section, but the primary way that I recommend you implement CoT prompting is via an explicit outlines of each step in a sequence of steps that the GPT would complete if it were successful. You can design the sequence so that (i) there are no user interactions in between the steps (that is, the GPT completes the sequence in one single action before responding to the user) or (ii) the GPT expects user prompts interspersed between each step.

The step-by-step instructions for our Feedback Accelerator are an example of (i). Here they are:

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