My Guide to Building Effective Custom GPTs
Since the capability was first released on ChatGPT, I have built custom GPTs at work and for fun, learning many things along the way. Here are the main basic and intermediate items I discovered.
I get asked all the time how I build my GPTs. So let’s set the record straight: I’m not giving away all of the ingredients to my secret sauce — but here’s a solid guide to get you started on building great custom GPTs inside ChatGPT.
One important note before you dive in: ethics matter. At every step. You should approach each iteration of your GPT like a new product — because that’s exactly what it is. Each version is a reflection of your design choices, your intent, and your standards. So take it seriously, review thoughtfully, and build responsibly.
1. Start with a Clear Use Case
Custom GPTs work best when they’re laser-focused on a specific task or purpose.
Ask yourself:
- What is the GPT supposed to do?
- Who’s going to use it?
- Is it something that default GPT models cannot do well?
✔️ Clarity here leads to better purpose + instructions — and fewer hallucinations.
2. Write Smart Instructions
Use Markdown formatting for better organization and readability. Highlight critical rules in ALL CAPS, like:
- ALWAYS use markdown when formatting output
- NEVER use web search unless explicitly required
- ALWAYS reason before responding
✔️ Reinforcing key behaviors upfront helps guide the model’s output.
💡 Important: Web browsing must be manually enabled — it’s off by default.
⚠️ Avoid using the “Create” tab in ChatGPT for building instructions. While it’s great for quick setups, it doesn’t offer full control over formatting, behavior rules, or layered logic. For precision, use GPT Builder or manually edit your custom GPT in the Configure tab.
3. Use Knowledge Documents or Custom Actions When Appropriate
If your content goes beyond ~8,000 characters, offload it into a Knowledge Document.
- NEVER repeat the same content in both Instructions and Docs
- ALWAYS enable Code Interpreter (aka Advanced Data Analysis) if the GPT needs to read files (or knowledge) or crunch data
🔎 Note: 8,000 characters is more of a guideline than a hard limit. The Knowledge tab can handle much larger inputs. You don’t need Code Interpreter just to upload a doc — only if the GPT needs to work with it.
4. Show the Output Format
If your GPT should follow a specific format, create a Markdown template to illustrate good output. Include variables such as:
{{user_name}}
{{date}}
{{custom_input}}
✔️ This enforces consistency and gives the GPT something to anchor its responses to.
5. Review and Improve
Use GPT Builder tools (like GPT Builder 😉) to:
- Spot gaps or inconsistencies in your instructions
- Get prompt suggestions to boost performance
- Refine logic for clarity and accuracy
Consider testing with both edge cases and typical user inputs. Don’t forget to ask:
👉 “Is this GPT behaving how I expect?”
👉 “Is it being helpful, or just verbose?”
6. Optimize & Finalize
To refine your GPT’s quality and performance, try:
- The o1 model for fast, balanced responses
- Deep Research GPT for more context-aware reasoning
- Writing GPT Tools to align tone and style
- Your own Custom GPT tools built for internal use
Merge your Instructions, Knowledge, and Prompt examples into a clean, integrated system.
✅ Test with real inputs
🔁 Iterate based on real results
✅ Quick Checklist
🔹 Clear Purpose
🔹 Smart Instructions
🔹 Effective Use of Knowledge Docs
🔹 Defined Output Format
🔹 Built-in Review Process
🔹 Model Optimization
To create this post, I wrote a checklist, organized it, and used my Writing Tone Clone GPT to manage it in my voice. After that, I made some edits by hand and with Grammarly for the final post.
Adam Mico
LinkedIn | Tableau Public | Writing Tone Clone GPT | Tableau Virtuoso GPT by Adam Mico | VizCritique Pro GPT | Data Mockstar by Adam Mico GPT | tBlueprint Navigator for Tableau Customer Success GPT
Note: My book, Tableau Desktop Specialist Certification, is available for order here.
