How to build a Custom GPT in 5 Easy Steps: A Beginners Guide

Ever since OpenAI introduced the concept of custom GPTs, everyone’s been racing to build their own AI assistants. But here’s the thing–people often are not satisfied with the results. Either the process becomes too complex or they are in a misconception that it’s too easy. 

To put it simply, creating a truly useful custom GPT that doesn’t just rehash generic responses is anything but straightforward. That’s where Weam AI comes into play, flipping the script on how to build a custom GPT. So buckle up! Whether you are seasoned AI users or just dipping toes into AI waters this guide will walk you through creating a custom GPT that actually does what you want it to do.

Understanding Custom GPTs and their Applications

Think of Custom GPTs like your sidekicks trained for specific tasks. The result obtained adds value in a specific way too. Custom GPTs have their own personality, knowledge base, and capabilities given by you. Approaching the idea this way makes it easy to understand how to build a custom GPT.

Also if you are thinking only ChatGPT is allowed to have GPTs, you are headed in the wrong direction. GPT stands for Generative Pre-trained Transformers. It changes the definition of how you see and utilize Custom GPTs.

• Definition: A version of GPT which is fine-tuned using a particular data set to perform a specific task. The tasks can belong to a requirement requested by an organization or can be used for personal needs. Considering its domain-specific knowledge, the output significantly differs from that of generic use of Gen AI.

• Example: Building a custom GPT for writing. It can provide content brief, suggest focus keywords, edit chunks of content for you. The data set used here can belong to fundamental guidelines, writing tone, paragraphing structure, ability to answer questions set by you.

A lot of take in, hence we have prepared a simple guide on how to build a custom GPT. But first let me show you a simple illustration explaining the concept of Custom GPTs.

Flow diagram 0.1

Not so simple for a beginner? Let us make one custom GPT by ourselves to get you a clear picture.

Before You Create Custom GPTs

One of the first things in the morning you do is brush your teeth. If I say you are doing it wrong, here is the correct way: make it a guide and post it in front of your bathroom mirror. Why bother? But if I say here is a toothbrush and a toothpaste which adapts your way of brushing teeth and gives fine results. The second option seems more convenient compared to the other one.

Similarly here are some of the attributes you have to consider. Understanding how to build a custom GPT does not come with a set of guidelines. It does not justify the idea correctly too.

1. Multi-step process: A custom GPT does multiple processes uniquely differentiating it from prompts.
2. Accuracy: You add interactive responses to strengthen the core concept for increasing the accuracy of the final result.
3. Versatile: Custom GPTs have a versatile nature, this comes from the multi-step process. For the beginning it can analyze the document and later it can provide you with multiple results.

Above mentioned attribute sets clear distinctions between custom GPTs and prompt. Moreover it highlights the difference between how to build a custom GPT vs an AI agent.

Keeping these small details in mind we are going to debunk the myth of misunderstanding what custom GPTs are built for. We’ll do this by building our own custom GPT.

Steps to Create Your Custom GPTs in Weam AI

I am going to use Weam.ai to create custom GPT. The reason is the simplification of steps related to how to build a Custom GPT. As mentioned in the previous section the whole

Weam dashboard to build custom GPT

The dashboard you view by navigating through the ‘Agent’ section in Weam appears something like this. Here you can find:-

1. Name: The name you give to your special Agent or custom GPT.
2. System Prompt: The multi-process prompt you provide to the agent
3. Goals: The response you expect from the Gen AIs to complete your task required.
4. Instructions: Additional instructions which will directly influence the response and your Agents behavior.

• Steps to build custom GPT or agents in Weam:-

• Step 1: Define purpose of your Agents or Custom GPTs & trigger.
• Step 2: Decide the workflow your Agents or Custom GPTs must follow.
• Step 3: Once you have all the information, enter them into the A to D fields.
• Step 4: Choose a model.
• Step 5: Additional documents or knowledge base.

Flow diagram 0.2

Illustration of steps to follow for easy Guidance

Recently one of my colleagues John (alias) said that he wanted to be active on LinkedIn. And I believe posting on social media platforms becomes a hassle for me. Many times even I don’t have the slightest idea what I should post about. However, keeping my LinkedIn account active is an important step to boost engagement.

So we helped set up a custom GPT in John’s Weam AI account. The core need was clear.

Step 1: Define Purpose & Trigger

For every week John needs a post for LinkedIn profile. His fundamentals were clear; I need to post about what I learned about Gen AI.

Purpose: To empower professionals by creating tailored, engaging, and platform-optimized content for LinkedIn, enhancing their online presence and networking opportunities.

Goal for Custom GPT: 

• Create AI-powered LinkedIn post generator
• Tailor content to specific professional profiles
• Generate engaging, platform-optimized content

Potential Triggers: Simple one word prompt which will activate the custom GPT. For example: Start, Go, etc.

Here I have predefined triggers: 

• Profile Input Triggers
• Content Strategy Triggers
• Contextual Triggers

Step 2: Determine Workflow

The overall workflow will include four weeks, four themes, and each theme will include 5 content types.

The four week theme framework will be something like:-

📅 4-Week Theme Framework:

• Week 1: AI for Personal Task Management
• Week 2: AI in Communication & Collaboration
• Week 3: AI for Learning & Skill Development
• Week 4: AI in Creative Problem Solving

The 5 content type I have selected are:-

Step 3: Configure Core Settings

No rocket science promise!  a simple intuitive easy to use dashboard to configure core settings.

• Name: LCPgpt (LinkedIn Content Production GPT)
• Goal: You are an AI-powered LinkedIn Content Generator specialized in creating engaging, educational posts about AI’s impact on professional productivity. Your goal is to produce a 4-week content series that explores AI’s transformative potential across different professional domains, using a structured yet dynamic approach.
• System Prompt: LinkedIn Custom GPT Weam AI
• Instructions:

1. Ensure content remains platform-appropriate for LinkedIn
2. Avoid overhyping AI capabilities
3. Provide balanced, realistic perspectives
4. Encourage user interaction and discussion
5. Stay current with AI productivity trends
6. Maintain professional yet approachable language

Content Quality Checklist:

• Originality
• Practical insights
• Audience relevance
• Engagement potential
• Ethical AI representation

Ethical Considerations:

• Transparency about AI’s capabilities
• Avoiding technological determinism
• Highlighting human-AI collaboration
• Respecting diverse professional experiences

Step 4: Model Selection

Choosing the right model depends on various factors. Currently there are various open source and proprietary models available in the market. The fierce competition is between ChatGPT Vs. Deepseek.

Weam AI allows access to a collection of powerful Gen AI models. Yes! The platform is a multimodal platform with an amazing set of features.

Anyway here are factors which affect your selection of the right Gen AI model.

1. Accuracy and precision

 Pick a model which has the ability to generate near perfect results. Obviously here the results are defined as not generic rather depending on the response you requested. Do consider models with minimal hallucinations rates and ability to maintain long contextual windows. The last two mentioned attributes are important as customGPTs perform multiple step processes.

2. Contextual Understanding 

In the field of AI it refers to an AI system’s ability to understand and interpret information by taking its environment, background, and intent into account.

3. Specialized Capabilities

Every LLM or Gen AI model has its own uniqueness. Claude is good at creative tasks, Gemini is a good resource collector, and ChatGPT has better analytical, logical and reasoning skills. Read all about comparisons between Gen AI models like ChatGPT Vs Gemini, Claude Vs ChatGPT, etc. It will help you understand which Gen AI model is better in terms of content generation for your custom GPT. To build a customGPT for writing, I’ve picked ChatGPT 4o.

4. Cost Considerations

When using it for personal use it is easy to decide which is the best for you. However when your entire team is using a Gen AI tool the cost can scale. In the midst of all these finding true value in your investment becomes a hassle. Hence cost is one of the primary factors that will influence your model selection process.

5. Practicality

A practical use case approach is the best way to land a decision. Clearly stating requirements, evaluating performance by taking various parameters into consideration. Lastly, conducting a comparative test will solidify your decision. 

If you feel the whole selection methodology is quite complex there are certain predefined approaches in choosing the right LLM model for you.

Step 5: Integrate Knowledge Base

Just like humans learn from their experiences, a knowledge base strengthens the Custom GPTs ability to generate insightful responses. A knowledge base integration empowers your custom GPT to:-

• Provide precise, domain-specific insights
• Generate more accurate and relevant content
• Move beyond generic responses to specialized understanding

Responses show difference between integrating knowledge base and not having a knowledge base

Best Practices for Using Custom GPTs

Strategic

A bit more emphasis on the nature when you begin to create Custom GPTs in Weam AI. Your unique approach meets best requirements and continuous improvement mechanisms.

1. Define clear, specific purpose: It ensures you are investing your resources and time on initiatives that are driving results. Well-defined purpose acts as a guide fostering innovation at the same time not disrupting current activities.
2. Understand Unique Organizational Needs: Optimizing tools according to your unique needs. Unique opportunities of growth are often hidden in relevant and best optimization of tools.
3. Iterative improvement mechanisms: Implementing iterative improvement mechanisms allows for continuous refinement and adaptation, ensuring long-term sustainability and relevance.

Technical

Particularly focusing on system prompt structure, prompt design principle, and integration checklist. The checklist accurately justifies your idea of how to build a custom GPT for particular purposes.

1. System Prompt Architecture: Design a comprehensive, layered prompt that clearly defines the GPT’s identity, capabilities, and interaction boundaries.
2. Instruction Precision: Create unambiguous, specific instructions with concrete examples to ensure consistent, targeted model performance.
3. Continuous Validation: Develop a rigorous testing framework that systematically evaluates the GPT’s reliability across diverse interaction scenarios.

Practical

Let’s go beyond technicality to address how to use Custom GPTs for the user-end side. Focusing on the real-world usability and responsible deployment of custom GPTs.

1. User Experience Design: Craft clear, intuitive initial instructions that immediately communicate the GPT’s purpose and expected interaction style.
2. Error Handling Strategy: Implement robust fallback mechanisms and clear guidance for scenarios where the GPT cannot complete requested tasks.
3. Privacy and Compliance: Carefully define boundaries for data handling, ensuring user interactions respect confidentiality and regulatory requirements.

Maintenance and Updates of Custom GPTs in Weam AI

Here are five quick tips to keep in mind after you are no more stuck at how to build custom GPTs. The tips are for the maintenance and updates.

1. Regularly Update Training Data: Ensure your custom GPT is trained on the most recent and relevant data. Regularly adding new datasets helps the model stay current with trends and information in your industry.
2. Monitor Performance Metrics: Continuously track key performance indicators such as accuracy, response relevance, and user satisfaction. Monitoring helps identify areas where the model may need tweaking or retraining.
3. Implement Version Control: Use version control systems to manage different iterations of your custom GPT. This allows you to track changes, compare performance over time, and roll back to previous versions if necessary.
4. Fine-Tune Hyperparameters Periodically: Reassess and adjust hyperparameters to optimize model performance. Fine-tuning can significantly enhance the effectiveness of your GPT without the need for extensive retraining.
5. Stay Informed About Updates in Weam AI: Keep an eye on the latest features, updates, and best practices provided by Weam AI. Just like this guide on how to build a custom GPT we also explore various capabilities of Gen AI platforms. Leveraging new tools and functionalities can improve your custom GPT’s capabilities and efficiency.

Why build custom GPTs in Weam AI

Weam AI is a productivity focused platform offering AI as a service. It allows you to access multiple LLMs, use those LLMs to make custom GPTs, and save your prompts in the prompt library. To streamline your workflow all these features are available in a shared and private workspace. 

We aim to foster innovation, collaboration, and help companies maintain their accelerated pace towards their goals. Now you don’t have to spend countless hours of meetings deciding on how to build a custom GPT. Weam AI democratizes and simplifies the process. Turning complex AI development into an intuitive, user-friendly journey. 

Conclusion

So this was our simple guide on how to build a custom GPT in Weam AI. Whether you’re solving niche business challenges or exploring creative applications, custom GPTs represent the frontier of personalized AI technology.  By breaking down barriers and providing robust tools, Weam empowers developers, entrepreneurs, and innovators to craft intelligent, specialized AI assistants.

The journey from concept to functional AI is now just a few strategic steps away, inviting you to reimagine what’s possible in the rapidly evolving landscape of artificial intelligence. Build your own Custom GPTs or helpful agents in Weam. Start for free!

FAQs