If you've been tinkering with custom GPTs, you're probably already amazed at their individual capabilities. But have you ever thought about the potential of stacking multiple GPTs together?

I'm talking about connecting multiple specialized GPTs to work together on bigger tasks. Each GPT is super good at one thing, and when you put them all together, they can tackle some pretty complex stuff that a single GPT may struggle to do.

How does it work in real life?

Let me share some cool ways I've been experimenting with this:

1. Content Repurposing

Imagine taking a single YouTube video and effortlessly transforming it into tailored content for LinkedIn, Facebook, Twitter, Instagram, email, and a blog post. Six different GPTs, each specialized for a specific platform, could work together to achieve this seamlessly.

2. Job Application Assistance

For job seekers, a stack of GPTs could revolutionize the application process. One GPT could analyze a CV against a job description, another could craft a personalized cover letter, and a third could generate interview preparation questions.

3. Educational Content Creation

Lecture recordings could be transformed into comprehensive study materials. One GPT could create a detailed blog post, another could generate mind maps, and a third could produce flashcards – all from the same source material.

4. Podcast Production

When preparing for a podcast episode, stacked GPTs could generate interview questions, write episode intros, and create announcement posts, all based on the guest profile and episode topic.

5. Business Planning

For entrepreneurs, a stack of GPTs could assist in creating a comprehensive business plan. Different GPTs could handle market research, customer persona creation, brand storytelling, marketing strategy, sales strategy, and even populate a Business Model Canvas.

So why am I so obsessed with this?

When specialized GPTs team up, the results can be impressive. It's like having a group of experts collaborating on a project, each bringing their unique skills to the table. The outcome? More comprehensive and nuanced results that often surpass what a single, general-purpose AI can do. They can also build upon each other's outputs, creating a more coherent and contextually relevant final product.

Another advantage of stacking GPTs is the speed it brings to the process. Instead of wasting time switching between different GPTs and copying and pasting information, you can establish a smooth workflow that gets you from start to finish much faster.

Furthermore, once you play around with enough commercial AI tools on the market right now, you’ll recognize many of them are essentially pre-configured stacks of GPTs designed for specific purposes. By learning to stack GPTs yourself, you can create custom solutions tailored to your exact needs without the overhead of multiple subscriptions. As your needs evolve, you can easily modify your GPT stacks or create new ones, providing a level of flexibility that off-the-shelf solutions often can't match.

But perhaps one of the most exciting aspects of stacking GPTs is the potential for innovation. By combining different AI capabilities in novel ways, you can create solutions to problems that might not have been feasible before.

How can you actually make this happen?

The great news is that stacking GPTs is more than just a concept; it’s entirely achievable.

You can bring this idea to life using MindPal - a platform that features a "multi-agent workflow." In this setup, each "agent" functions like a custom GPT, allowing you to connect them into a multi-step AI workflow, with each step handled by a specialized agent.

Here's how you can get started:

1. Explore Templates: Visit MindPal's landing page to browse a variety of template workflows. These pre-configured setups can give you ideas and a starting point for your own workflows.

2. Create Custom Agents: Build specialized AI agents - aka GPTs within MindPal, tailoring them to your specific needs by providing instructions and training data.

3. Design Your Workflow: Connect your custom agents in a logical sequence to create a multi-step process that addresses your complex tasks.

4. Test and Refine: Run your workflow with sample inputs, analyze the results, and refine the process as needed to optimize performance.

5. Scale and Automate: Once your workflow is perfected, use it to handle larger volumes of work or integrate it into your regular business processes.

To see how easy it is to create and implement AI workflows with MindPal, check out this 1-minute video showcasing the entire process:

Conclusion

Multi-agent AI workflows represent a significant leap forward in how we can leverage artificial intelligence to enhance productivity and innovation. By stacking GPTs, we can create powerful, flexible, and efficient systems that tackle complex tasks with ease. Whether you're looking to streamline content creation, automate business processes, or develop innovative solutions to challenging problems, multi-agent workflows offer a promising path forward.

As you embark on your journey with multi-agent AI workflows, remember that the key to success lies in creative thinking and continuous experimentation. Don't be afraid to mix and match different AI capabilities to see what works best for your unique needs. With platforms like MindPal making it easier than ever to create and manage these workflows, the potential for innovation is limitless.