AI Trends: Mastering ChatGPT’s New Custom GPTs with Advanced Techniques to Win Back Time

In the rapidly evolving world of AI Trends, OpenAI’s refreshed custom GPTs are making waves with powerful updates that go beyond the…

Robert Gatchel

— 5 min read
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In the rapidly evolving world of AI Trends, OpenAI’s refreshed custom GPTs are making waves with powerful updates that go beyond the basics. Today, we delve into advanced techniques that help us save precious time by harnessing these new capabilities. From context stacking and the remarkable o3 model to custom actions connecting third-party data, we explore how to build smarter, more efficient GPTs that truly work for us.

Table of Contents

Understanding the Evolution of Custom GPTs

Custom GPTs, introduced by OpenAI nearly two years ago, serve as tailored versions of ChatGPT that fit specific workflows and needs. Unlike the generic ChatGPT experience, these specialized models allow us to create smaller, smarter assistants that can be reused anytime without repeatedly providing the same instructions or data.

Initially, these GPTs faced limitations, particularly because they couldn’t fully exploit the latest ChatGPT updates. However, recent changes have unlocked new potentials, enabling us to fine-tune performance across different industries and tasks. This evolution empowers us to build GPTs that are not only customized but also capable of complex reasoning and tool usage.

Key Updates to Custom GPTs

  • Creators can now select from the full range of ChatGPT models — including GPT-4, GPT-4o, GPT-3.5, GPT-4 mini, and more — when building custom GPTs.
  • Recommended models can be set to guide users toward the most efficient choice for their needs.
  • Custom actions, which allow integration with third-party APIs, require the use of GPT-4o or GPT-4.1 and a paid subscription to build.
  • Usage of these GPTs is available on free plans, but modifications require a paid plan.

While some may hesitate to pay for these subscriptions, the nominal monthly fee is a bargain compared to the productivity gains and business growth opportunities they unlock.

Mastering Context Stacking: The Secret to Smarter Conversations

One of the most exciting advanced techniques we explore is context stacking, a term we’ve coined to describe the ability to switch between different GPT models within the same conversation without losing context. This is a game-changer, especially compared to other AI platforms like Google Gemini or Claude, where switching models forces you to start fresh.

Here’s why it matters: on a ChatGPT Plus plan, the context window can hold up to 32,000 tokens (about 28,000 words). This means the model remembers the last 28,000 words of your conversation. If your dialogue exceeds this, older parts begin to drop off. Context stacking allows us to use various GPTs or models within this window seamlessly, maintaining continuity and building on previous insights.

This technique is especially powerful when combined with custom GPTs, as it allows us to bring in specialized knowledge or capabilities mid-conversation, boosting efficiency and creativity.

Leveraging the O3 Model for Advanced Reasoning and Planning

Alongside context stacking, the introduction of the o3 model marks a significant leap in AI capability. Unlike earlier transformer models, the o3 series exhibits agentic behavior, meaning it can think, reason, plan ahead, and use different tools autonomously. This makes it ideal for complex tasks, such as data analysis, iterative research, and logical problem-solving.

For example, we built a custom GPT called the Everyday AI Episode Researcher that uses the o3 model to analyze podcast data and plan out new episode ideas. It runs Python scripts to dig into data files, extract insights, and generate actionable plans — all within the same chat context.

Another custom GPT we developed is the Evergreen Episode Hunter, which identifies podcast episodes with lasting appeal by analyzing listener trends over time. This GPT not only finds outlier episodes with long-term value but also autonomously researches how to update and improve them based on recent developments.

The o3 model allows this GPT to engage in multi-step reasoning: it reviews data, searches the web for new information, recalculates, and rechecks its findings — much like a human researcher would.

Custom Actions: Bringing Third-Party Data into Your GPTs

To unlock even greater potential, we explore custom actions in GPTs, which enable integration with external APIs. This capability allows GPTs to fetch live, dynamic data or perform actions like writing to calendars and sending emails.

Building these integrations might sound technical, but OpenAI provides an actions GPT that guides you step-by-step through the process. You can paste API documentation, and it will generate the necessary JSON or YAML schema for your GPT.

For those less familiar with APIs, Zapier offers a user-friendly solution. Zapier connects thousands of services and provides ready-made schemas that you can import directly into your GPTs. This means you can add functionality like scheduling meetings or replying to emails without writing any code.

As a practical example, we created a GPT that adds events to Google Calendar using Zapier. A simple prompt like “Add a 30-minute meeting with Jake this Saturday at 5 PM” triggers the action, and the event appears in the calendar instantly.

Managing Context Windows and Tokens

Understanding how tokens and context windows work is crucial for optimizing GPT usage. A token roughly corresponds to a piece of a word, and GPTs have limits on how many tokens they can process at once.

While there’s no straightforward way to know when the context window is full during a conversation, using browser extensions that count tokens can help monitor usage. Without this, you might notice the model forgetting earlier parts of your conversation once the limit is exceeded.

Importantly, when switching models within ChatGPT, the context window does not reset. The conversation retains its history, allowing for smooth transitions between models without losing information.

OpenAI’s updates to custom GPTs, especially the introduction of context stacking, the o3 model, and custom actions, represent a significant advancement in AI Trends. These features empower us to build intelligent, adaptable assistants that save time, enhance productivity, and bring dynamic data directly into our workflows.

Whether you’re a business leader, content creator, or AI enthusiast, mastering these techniques opens new pathways to leverage AI in meaningful ways. The key is to experiment, observe the GPTs’ chain of thought, and iteratively refine your custom instructions to get the best results.

As we continue to explore AI Trends, let’s embrace these powerful tools and build the future of work together.

Frequently Asked Questions (FAQ)

What is context stacking, and why is it important?

Context stacking refers to the ability to switch between different GPT models within the same conversation without losing the chat history or context. This allows for more flexible and efficient interactions, as you can leverage different models’ strengths without starting over.

What makes the o3 model different from previous GPT models?

The o3 model exhibits agentic capabilities — it can think, reason, plan ahead, and autonomously use tools. This makes it superior for complex tasks such as multi-step reasoning, data analysis, and iterative research.

Do I need to be technical to build custom GPTs with API integrations?

Not necessarily. While understanding APIs helps, tools like OpenAI’s actions GPT and Zapier simplify the process by generating schemas and providing visual interfaces to connect GPTs with third-party services.

Can I use custom GPTs on a free ChatGPT plan?

You can use custom GPTs on a free plan, but building or modifying GPTs with advanced features like custom actions requires a paid subscription.

How do tokens affect my GPT conversations?

Tokens are pieces of words that GPT processes. Each GPT has a maximum token limit for its context window, which determines how much conversation history it can remember. Monitoring token usage helps prevent losing earlier conversation context.

This article is based on comprehensive research derived in part from the referenced video Ep 563: ChatGPT’s New Custom GPT’s: Advanced techniques to win back time

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