Open AI’s New GPTs - How to Create & Share!
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GPT Builder lets creators define a custom assistant’s purpose and behavior using natural language, with real-time updates to the GPT configuration.
Briefing
OpenAI’s new GPTs feature is rolling out a way for non-developers to build customized versions of ChatGPT—then share them via links—using a guided “GPT Builder” workflow. The core value is practical: creators can define a GPT’s purpose in plain language, set its behavior and guardrails, and optionally attach tools like web browsing, image generation, file-based knowledge, and custom API actions. That combination turns a chat model into a more specialized assistant, from creative image generators to real-world decision helpers.
The walkthrough starts with the “Create a GPT” beta on the OpenAI site, where a left-side builder and right-side live preview update together. A simple example—“comic book image Creator”—shows how natural-language prompts can generate a name, starter suggestions, and even a profile picture using DALL·E 3. The builder then tailors behavior through an instruction set: the GPT is designed to produce comic-style visuals, guide users on character design, settings, and plot visualization, and keep outputs strictly within the comic/illustrative format. The creator also chooses how the assistant should handle ambiguity (ask for clarification vs. use judgment) and sets the communication style (casual and friendly).
A key part of the configuration is capabilities. The creator can upload files as “knowledge” so the GPT can reference specific documents or datasets for that custom use case. Capabilities can include web browsing, DALL·E image generation, and code interpreter (left enabled in the example). Most notably, the “actions” section allows a GPT to call external APIs—effectively enabling agent-like workflows such as retrieving data or triggering services—provided the creator sets up authentication (e.g., API keys). This is positioned as the path to “superpower” GPTs that do more than chat.
After testing in the playground, the GPT can be saved and published with different visibility settings: only the creator, anyone with a link, or public (with a note that public listing in the GPT store is expected in the coming weeks). Sharing is immediate via a copyable URL, and the GPT appears in the creator’s Explore sidebar so it can be edited, deleted, or opened for new chats.
The transcript also demonstrates a more functional GPT, “Auto advisor,” aimed at car buying. By uploading a screenshot of a car listing, the GPT breaks down the listing and uses web search to check pricing context and reliability—though it struggles when key variables like location aren’t provided. The creator highlights that entertainment and utility both benefit from the same customization pipeline.
Finally, community-made GPTs are tested from a “share gpts” area on the creator’s Discord: “grug’s wisdom” (caveman-style replies with web search), “deal Scout” (finding a Best Buy deal via web search), and “GPT Genesis” (a GPT idea generator). The overall takeaway is that the barrier to building and distributing specialized assistants is low enough that many users will experiment immediately, even before a formal GPT store launches.
Cornell Notes
OpenAI’s GPTs feature lets users create customized ChatGPT assistants through a guided GPT Builder interface, then share them via link or public listing. Creators define purpose and behavior in natural language, which can automatically generate names, starter prompts, and even profile images using DALL·E 3. The configuration supports “knowledge” uploads (files the GPT can reference), capabilities like web browsing and image generation, and “actions” that let GPTs call external APIs with authentication—enabling more agent-like workflows. Examples include a comic-book image generator and a car-buying “Auto advisor” that analyzes listings and uses web search, with limitations when location data is missing. Community GPTs such as grug’s wisdom and deal Scout demonstrate how quickly people are turning customization into niche tools.
How does the GPT Builder turn a simple idea into a working custom assistant?
What does “capabilities” change for a custom GPT, and why does it matter?
What is the practical role of “knowledge” uploads?
Why are “actions” considered the biggest upgrade beyond chat?
What limitation shows up in the car-buying “Auto advisor” example?
How do community GPTs demonstrate the feature’s range?
Review Questions
- What specific configuration elements (instructions, capabilities, knowledge, actions) most directly control a GPT’s behavior and usefulness?
- In the examples given, where does web browsing help—and where does it still fail without additional user-provided context?
- How does link-based sharing change the speed at which GPTs can be tested and iterated compared with waiting for a public store?
Key Points
- 1
GPT Builder lets creators define a custom assistant’s purpose and behavior using natural language, with real-time updates to the GPT configuration.
- 2
Custom GPTs can generate assets like profile pictures using DALL·E 3 as part of the creation flow.
- 3
“Knowledge” uploads allow a GPT to reference specific files for consistent, use-case-specific answers and outputs.
- 4
Capabilities such as web browsing and DALL·E expand GPTs beyond text into research and image generation.
- 5
Actions enable GPTs to call external APIs with authentication, making automated, agent-like workflows possible.
- 6
Publishing supports multiple visibility levels, including link-only sharing before the GPT store fully launches.
- 7
Community-created GPTs demonstrate rapid experimentation across entertainment (grug’s wisdom) and utility (deal Scout, Auto advisor).