Custom GPT

A Custom GPT is a tailored version of ChatGPT that you configure for a specific purpose. Instead of using the general-purpose ChatGPT, you create a version with custom instructions, uploaded knowledge documents, and specific capabilities — all without writing any code.

How Custom GPTs work

When you create a Custom GPT, you configure:

• Instructions: Detailed guidance on how the GPT should behave, what role it should play, and how it should respond. Essentially a permanent system prompt.
• Knowledge files: Documents you upload that the GPT can reference when answering questions. Up to 20 files covering your specific domain.
• Capabilities: Enable or disable features like web browsing, code execution (Code Interpreter), and image generation (DALL-E).
• Actions: Connect to external APIs so the GPT can retrieve data from or send data to other systems.

Common use cases

• Subject matter expert: Upload your company's documentation and create a GPT that answers employee questions based on official policies
• Writing assistant: Configure a GPT with your brand voice guidelines and content templates
• Research assistant: Create a GPT that follows your specific research methodology and citation standards
• Customer-facing chatbot: Build a product support GPT with your FAQ and troubleshooting guides
• Coding assistant: Configure a GPT with your codebase conventions and architecture decisions
• Training tool: Build a GPT that quizzes employees on company procedures

Creating a Custom GPT

The creation process uses natural language — you describe what you want and ChatGPT helps you configure it:

1. Navigate to "Explore GPTs" in ChatGPT
2. Click "Create"
3. Describe your GPT's purpose in the GPT Builder chat
4. Upload relevant knowledge documents
5. Configure capabilities and actions
6. Test and refine
7. Publish (privately, to your organisation, or publicly)

Limitations

• Knowledge file limits: File uploads are size-limited and the GPT may not perfectly recall all content from large documents
• No fine-tuning: Custom GPTs use the same underlying model as ChatGPT — they add instructions and context, not model-level customisation
• Reliability: Responses still carry the risk of hallucination, especially when questions fall outside the uploaded knowledge
• Platform dependency: Custom GPTs only work within OpenAI's ecosystem

Custom GPTs vs fine-tuning vs RAG

• Custom GPTs: Easy to create, no coding, limited to ChatGPT's interface. Best for individual or small team use.
• RAG systems: More technically complex but offer better control over retrieval and can be integrated into any application.
• Fine-tuning: Changes the model's underlying behaviour. Best for specialised tasks at scale.

Custom GPTs are the most accessible way to create a purpose-built AI assistant. For more demanding enterprise applications, RAG or fine-tuning may be necessary.