Trying to use generative AI chatbots to create on-brand content at scale is proving harder than we once thought. The second you need consistency across routine content workflows, its limitations start to rear their heads.
Custom GPTs are microbots that help alleviate prompting and loss-of-context issues, giving you more direct routes to reliable content than basic generative AI engines do.
Let’s explore what they are, what they’re good for and how to create one.
What Does GPT Stand For?
GPT stands for generative pre-trained transformer. It’s a class of neural network models optimized for natural language processing. Popularized by applications like ChatGPT, these AI agents produce human-like text, code and synthetic media.
Here’s a breakdown of the acronym:
- Generative: Instead of categorizing existing data, GPTs can generate new, original content, like essays, answers to questions and imagery.
- Pre-trained: These engines undergo a training phase on massive datasets, drawing on billions of web pages and books to learn the nuances of human speech, including grammar, syntax and facts.
- Transformer: GPTs have a deep learning architecture that enables them to understand the semantic relationships between words and concepts. This is why they can retain contextual information and engage in complex discussions.
Working with a massive, publicly available (free) model only grants access to a certain number of tokens, which basically means you’re working with a sycophant with a limited attention span. That’s where challenges arise. A few prompts into any project, and your original brief is all but gone, requiring further prompt engineering.
Of all the automation benefits chatbots provide, this tiny spec of an attention span is among the most dire shortfalls of publicly available models. But you can work around it, and to do so, all you need is to call on their little brother: Custom GPTs.
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What Is a Custom GPT?
A custom GPT is a large language model (LLM) that users can train to perform a specific task in the way they need it done. By building these custom specs into the GPT’s operational baseline, users no longer have to repeatedly provide context as they might with ChatGPT.
So, say you’re at an agency with a portfolio of 20 clients, or you’re working in-house with 20 different tasks on your table. You can train a custom GPT for each client or task by adding briefs, specs, and proprietary knowledge files* and connecting them to external APIs. Then, because your GPT already understands the assignment, you can just go ahead and automate. Anyone with a paid subscription to OpenAI can have virtually unlimited custom GPTs.
From a content production standpoint, it works similarly to contentmarketing.ai, where you add your brand guidelines, target audience and writing briefs once, and the AI follows those exact instructions for each piece of content. The catch is that you do have to set them up (which is quick and requires no coding experience), and you still have to work through the project with them, just with less effort than with ChatGPT.
If creating your own content from scratch every time seems like too much of a stretch, look for platforms like contentmarketing.ai, which use a series of agentic bots to handle the content generation process for you.
*Note: Paid OpenAI users’ data is excluded from training, but make sure you understand the data privacy policies before uploading sensitive information.
ChatGPT vs. a Custom GPT: Which Should You Use?
ChatGPT is a flexible generalist with limited memory. Custom GPTs are highly specialized task executors with limited flexibility. Naturally, each has its own strengths and weaknesses based on what you need done.
What ChatGPT Is Good For
ChatGPT’s merits lie in two places, the first being one-off projects. When you need three social posts to promote a client’s asset one time, you’ll probably get as much out of ChatGPT’s base model as you would from fine-tuning a custom GPT.
The second of its strengths is versatile production. Say you’re using ChatGPT for a complex sales funnel that brings several niches within your audience to conversion; ChatGPT offers the elasticity to create messaging specific to each audience group at different stages, while maintaining a level of consistency across the funnel. Not without your expert input, of course.
What Custom GPTs Are Good For
Custom GPTs help you maintain consistency for specific tasks you’ll do more than once. For instance, drafting routine content for a client requires a sustained brand voice and delivery across multiple channels and assets. Personal assistants and travel planners need a seamless pickup every time they use generative AI chatbots to do client work. Semantic Q&A sessions for internal documents call for a high level of accuracy.
These are all examples of what custom GPTs excel at: their automation and accuracy are far more nuanced than their handyman big brother. Here’s why many marketers choose custom options over ChatGPT:
- Customization: You can automate workflows and produce content however you need it done.
- Consistency: Custom GPTs follow defined processes exactly as you program them to.
- Accuracy: Using your own data as the baseline reduces the risk of hallucinations and drift, making results much more predictable.
- Tool integration: You can connect them to APIs, websites and external databases to broaden their knowledge base and capabilities.
- Scalability: Organizations can share custom GPTs internally, helping with customer support or document searches without adding headcount.
- Data analysis: Custom GPTs use your knowledge as their baseline, so they tend to outshine ChatGPT’s data analysis capabilities.
How To Create a Custom GPT Step by Step
1. Define the GPT’s Purpose and Target Audience
Determine a clear focus. Use this stage to prep your GPT description(s), which should explain the GPT’s purpose, who it’s for and the specific tasks you want help with. A clear intent helps users understand what the GPT does immediately.
2. Open ChatGPT and Go To the “Create a GPT” Builder
Head to your web browser and open Explore GPTs in the ChatGPT sidebar. Select Create to launch the GPT Builder. Just know that while you can use custom GPTs on mobile, building and editing are only available from a desktop or laptop browser.
3. Name the GPT and Write a Short Description
Choose a clear name that represents your GPT’s function. This is what users will see in search results, the GPT Store, shared links and at the top of the chat. Plug in the description you wrote so it appears in previews and store listings before users open it.
4. Add Custom Instructions for Tone, Behavior and Expertise
Write structured instructions to guide how the GPT behaves and makes decisions. Here are a few tips for providing direction:
- Headings and lists make your priorities and steps easier to identify.
- Make sure you deploy explicit step-by-step structures (e.g., When X happens → do Y) for multi-step workflows.
- When you’re writing commands, go for concise, positive ones (e.g., “Do X”) rather than long lists of restrictions (e.g., “Don’t do Y”).
- To clarify expectations, add a few brief examples of acceptable and unacceptable outputs.
5. Upload Reference Files or Knowledge Sources (if Needed)
You can upload up to 20 files (up to 512 MB each) to the knowledge base to act as source material during conversations. Only use knowledge for reference material and do not use it for behavioral rules. Those belong in the instructions.
Opt for clear, text-forward files and avoid complex layouts where possible. If you want the GPT to cite or quote these materials, spell it out in the instructions.
6. Configure Capabilities
Toggle on the built-in capabilities you need. These include real-time web search, image generation, canvas (for creating structured content), and code interpreter and data analysis.
If you want to connect your custom GPT to external APIs, head to Actions. Just note that a GPT can use either Apps or Actions, but not both at once. You can also specify a recommended model to point users toward the best option for the task when they open a chat.
7. Add Suggested Conversation Starters for Users
Configure conversation starters (the example prompts that appear when you open GPT). Give a few real-world and high-value prompts that reflect the GPT’s intended use. This also helps users understand what they can ask for.
8. Test the GPT
Navigate to the Preview pane to take the GPT for a spin. Look for tone and accuracy, and confirm that it uses any uploaded knowledge files as expected.
9. Troubleshoot Instructions and Settings Based on Testing
Now, before you connect more tools or features, work to resolve any undesirable behaviors or outputs by adding or editing your instructions and examples. Any changes you make are automatically saved as a draft while you edit.
10. Save and Publish the GPT (Private, Link-Only or Public)
To apply the edits, select Create (for a new GPT) or Update (for an existing one). If you want to restore an earlier version of your draft, head to the version history from the (•••) menu.
11. Share the GPT With Users or Your Team and Monitor Feedback
You can manage access by selecting Share in the editor. Alternatively, use the (•••) menu to copy the GPT link. If you want to make further updates after soliciting feedback, just go to Explore GPTs → My GPTs → choose the GPT → Edit GPT.
Custom GPTs: Helping Automate Automation
While generative AI has been an incredible step forward, the go-to-market (GTM) content promises aren’t quite there yet.
Custom GPTs will not hit GTM copy 100% of the time, but they can save you the hassle of manually setting up context every time you want to create on-brand content — which is already a giant leap ahead of base models.
Note: This article was originally published on contentmarketing.ai.
