Beyond Basic Prompts: Understanding AI Platform Extensions and When to Use Them
My client asked me what GEMS, GPTs, and Artifacts were, and I realized we've reached Peak AI Buzzword Bingo. Turns out they're actually useful - here's your survival guide!
Last week, I was working with a client who was confused about all the additional features that major AI platforms keep rolling out. "I just figured out how to write decent prompts," they told me, "and now there are all these other things to learn about. GPTs, artifacts, GEMS... what are these things and do I actually need them?"
It's a fair question. The AI landscape has evolved rapidly, and the major platforms (ChatGPT, Claude, and Gemini) have all developed specialized tools beyond their core chatbot functionality. These extensions can be powerful accelerators for business users who understand how to leverage them. The challenge is knowing which tool fits which situation, and when the basic chat interface is still your best option.
I created this practical guide to help you navigate these options. Let's break down what each platform offers, with real examples of when and how to use them.
ChatGPT's Extension Ecosystem: Custom GPTs, Projects, and Canvas
OpenAI has built several ways to extend ChatGPT's capabilities, with varying degrees of customization and complexity.
Custom GPTs are specialized versions of ChatGPT that you can create for specific tasks or domains. Think of them as pre-configured ChatGPT instances with custom instructions, knowledge, and capabilities. I've found these incredibly useful for standardizing repetitive workflows.
For example, I created a custom GPT for my content team, including our brand guidelines, tone preferences, and formatting requirements. Instead of sharing a 20-page brand document with new writers and hoping they follow it, they can access our custom GPT, which guides their work consistently. It's like embedding your expertise into the AI so others can benefit from it without you needing to be present.
The business value here is significant: Custom GPTs allow you to encode institutional knowledge and standardize AI usage across teams. They're ideal when you repeatedly give the same context or instructions to the base ChatGPT model.
ChatGPT Projects provide a workspace for more complex, multi-stage tasks. Unlike single conversations, Projects allow you to organize work into separate sections while maintaining context across them. This addresses one of the major limitations of chat interfaces: the difficulty of working on complex deliverables with multiple components.
I recently used Projects to develop a comprehensive market analysis report. I created separate sections for competitor research, SWOT analysis, pricing strategy, and go-to-market recommendations. The ability to revise each section independently while maintaining the overall context saved me hours of work compared to a linear chat.
Projects are particularly valuable for business tasks that require:
Multiple interconnected deliverables
Refinement of different sections at different times
Maintaining organizational structure for complex outputs
Canvas is ChatGPT's visual workspace that allows for more free-form creation and editing. It's essentially a document-based interface rather than a chat-based one, which makes it much better suited for drafting and refining substantial content.
I've found Canvas particularly useful for creating client deliverables like proposals and reports. The ability to edit text directly rather than going through the chat's back-and-forth considerably speeds up the refinement process. Canvas excels when you need precise control over formatting and structure.
Gemini's Offerings: GEMS and Canvas
Google's Gemini has its own unique extensions that leverage Google's ecosystem strengths.
GEMS (Gemini Extensions) are specialized functions that enhance Gemini's capabilities by connecting it to other tools and data sources. These include extensions for web searches, data analysis, and code execution.
The business applications here are practical and immediate. For instance, I recently used Gemini with extensions to analyze a dataset from our CRM system. Gemini could pull in the data, run analyses, and share the results without me needing to switch between different applications. This integration capability is where GEMS truly shines (I'm looking forward to when it adds visualization capabilities in GEMS, as it already exists in regular 2.5 Pro chats!)
When to use GEMS? They're ideal for tasks that require connecting multiple data sources or tools. If you find yourself constantly switching between applications to complete a workflow, GEMS might offer a more streamlined solution. I'll give you a perfect real-world example I used the other day. I work with a large number of law firms on using AI, and I wanted to combine the latest YouTube videos out there with my content, so I built a GEM to do just that (in fact, it works on any topic). It's a super powerful way of connecting different data sources.
Gemini Canvas, similar to ChatGPT's Canvas, provides a document-style interface for working with Gemini. Its tight integration with Google Workspace sets it apart, making it particularly valuable for teams already working within the Google ecosystem.
The ability to edit portions of a document is a game changer and transforms the experience of creating content. The familiar Google Docs-like interface reduces the learning curve for teams already comfortable with Google's tools.
Claude's Artifacts: A Different Approach
Anthropic's Claude takes a somewhat different approach with Artifacts. Rather than creating separate tools, Claude artifacts are structured outputs generated within the chat interface. These can include code, documents, visualizations, or other specialized content.
The key advantage of Claude's approach is simplicity. Instead of learning multiple interfaces, you work within the familiar chat environment while producing more sophisticated outputs.
Claude's artifacts are particularly valuable for generating code, visualizations, and technical documentation. Recently, I needed to create a visualization dashboard for client data. With Claude, I could describe what I needed conversationally, and it generated the complete code as an artifact that I could immediately implement.
Artifacts excel when you need:
Self-contained, executable code
Structured documents like reports or specifications
Visualizations and diagrams
Content that will be used outside the conversation
The limitation is that artifacts exist within the chat flow, making organizing multiple related artifacts more challenging than in a dedicated workspace like Projects or Canvas.
Making the Right Choice: A Practical Framework
With all these options, how do you choose the right tool for your business needs? I use a simple framework based on three factors:
Complexity of the task: Basic chat interfaces are still the fastest for simple, one-off questions. For multi-component projects, choose Projects or Canvas.
Need for customization: If you repeatedly provide the same context or guidelines, invest in Custom GPTs to standardize your approach.
Integration requirements: GEMS offers the strongest integration capabilities if your workflow spans multiple tools or data sources.
Looking Forward
These platforms continue to evolve rapidly. Just last month, I saw significant updates to artifacts that expanded their visualization capabilities. The trend is clear: AI platforms are moving beyond simple chat interfaces toward more powerful, specialized tools for complex work.
For business users, my advice is straightforward: start with the specific problems you're trying to solve, then determine which of these tools best addresses those needs. Don't fall into the trap of using an advanced feature just because it exists.
Have you experimented with any of these AI extensions in your work? I'd love to hear about your experiences and whether you've found particular approaches more valuable for specific business contexts. The real power of these tools emerges when we apply them thoughtfully to our unique business challenges.