Building Custom GPTs for Your Workflow

Prerequisites

• ChatGPT Plus subscription (yes, you have to pay. I know.)
• Clear use case in mind
• Example outputs to guide the GPT
• A tolerance for talking to a machine like its your colleague
• At least one mass-produced energy drink within arm’s reach

What We’re Building

Custom GPTs in ChatGPT that actually understand your specific context. Code reviewers that know your style guide. Writing assistants trained on your voice. Analysts familiar with your data. Basically, little digital minions that do what you tell them without filing an HR complaint.

If you’ve ever pasted the same 400 word system prompt into ChatGPT for the fifteenth time this week, congratulations, you’ve been doing this the hard way. Custom GPTs let you bake all of that context in once and never think about it again.

The Approach

1. Define the purpose
2. Write instructions
3. Add knowledge files
4. Configure capabilities
5. Test and iterate

Simple enough on paper. The devil, as always, is in step 2.

Step 1: Choose a Focused Purpose

Bad: “General coding assistant” Good: “TypeScript code reviewer for React applications following our team’s style guide”

Bad: “Writing helper” Good: “Technical blog post editor that matches my conversational writing style”

Narrow scope equals better results. Every time. The moment you try to make a GPT that does everything, it does nothing well. You wouldnt hire a plumber to do your taxes, so dont ask your code reviewer GPT to also write poetry.

Step 2: Write Clear Instructions

This is where the magic happens. Think of it as writing a job description for someone who is extremely eager, frighteningly literal, and has zero institutional knowledge.

You are a TypeScript code reviewer specialising in React applications.

## Your Role
Review code for:
- Type safety issues
- React anti-patterns
- Performance problems
- Accessibility concerns

## Style Guide
- Use functional components only
- Prefer named exports
- Use absolute imports with @ prefix
- Keep components under 100 lines

## Response Format
For each issue found:
1. Quote the problematic code
2. Explain the issue
3. Show the corrected version
4. Rate severity: 🔴 Critical | 🟡 Warning | 🔵 Suggestion

## Personality
Be direct and constructive. Skip pleasantries.

The more specific you are, the less you’ll be arguing with it later. “Be direct and constructive” saves you from getting a three paragraph essay about how your code is really great actually, but maybe, just possibly, there might be one tiny little thing worth considering.

Step 3: Add Knowledge Files

Upload reference documents:

• Style guides: Your team’s coding standards
• Examples: Good code you want to emulate
• Context: Architecture decisions, README files
• Templates: Standard patterns you use

File formats: PDF, TXT, MD, DOCX, CSV

Example for a code reviewer:

• style-guide.md
• architecture.md
• good-examples/ (combined into one file)

This is the bit most people skip, and then wonder why their GPT gives generic answers. Feed it your actual documents. Give it real examples from your codebase. The more context it has, the less you’ll want to throw your laptop out the window.

Step 4: Configure Capabilities

Web Browsing: Enable for research-heavy GPTs

• Good for: Fact-checking, staying current
• Bad for: Speed-critical tasks

DALL-E: Enable for visual tasks

• Good for: Diagram generation, mockups
• Bad for: Code-focused GPTs (it doesnt need to draw pictures of your API)

Code Interpreter: Enable for data analysis

• Good for: CSV analysis, calculations
• Bad for: Simple Q&A GPTs

Pick only what you need. Every capability you enable is another avenue for the GPT to go off on a tangent. Less is more.

Step 5: Conversation Starters

Pre-populate useful prompts:

- Review this React component for issues
- Explain why this code might be slow
- Suggest accessibility improvements
- Convert this class component to hooks

Makes it immediately clear how to use the GPT. Think of these as the “suggested searches” of your custom assistant. People are lazy (myself very much included), and having a button to press is always better than a blank text box.

Step 6: Example GPT Configurations

Here are a few I’ve built that actually get daily use. Feel free to nick them.

Technical Writer

You are a technical writing editor who helps improve documentation.

## Style
- Clear, concise prose
- Active voice
- Short sentences and paragraphs
- No jargon without explanation

## Process
1. Read the entire document
2. Identify clarity issues
3. Suggest specific rewrites
4. Explain why each change helps

## Output Format
Use track-changes style:
- ~~Original text~~ → Suggested replacement
- [Note: explanation]

Code Explainer

You explain code to developers who are learning.

## Approach
- Start with the big picture
- Break down into logical sections
- Explain why, not just what
- Use analogies when helpful

## Format
1. One-sentence summary
2. Step-by-step walkthrough
3. Key concepts highlighted
4. Common gotchas mentioned

This one isnt just for juniors, by the way. I use it when I’m staring at legacy code that predates my involvement (and possibly civilisation itself).

Meeting Summariser

You summarise meeting transcripts for busy people.

## Output Structure
1. **Decision**: What was decided?
2. **Actions**: Who does what by when?
3. **Discussed**: Key topics covered
4. **Open**: Unresolved questions

## Rules
- Keep under 200 words
- Use bullet points
- Include names for accountability
- Flag blockers explicitly

Absolute lifesaver. Paste in a transcript, get back something you can actually send to the team without anyone falling asleep reading it.

Step 7: Testing Your GPT

Test edge cases:

1. Happy path: Normal use case
2. Garbage in: Malformed input
3. Edge cases: Unusual requests
4. Adversarial: Attempts to bypass instructions

Refine instructions based on failures. And there will be failures. The first version of every custom GPT I’ve built has been, charitably, “not great.” That’s fine. You iterate.

Step 8: Sharing and Publishing

Private: Only you can access Anyone with link: Share with team Published: Listed in GPT store

For team GPTs, document:

• What it does
• How to use it
• Known limitations
• Who to contact for issues

That last point is important because “who to contact” will inevitably be you, and you’ll want people to at least try the documentation before pinging you on Slack at 9pm.

Step 9: Iteration

After a week of use:

1. Review conversation history
2. Find common failures
3. Add examples to instructions
4. Update knowledge files
5. Test again

Custom GPTs improve with feedback. Think of the first version as a rough draft. By version three or four, you’ll have something genuinely useful. By version ten, you’ll wonder how you ever worked without it.

What I’d Do Differently

Include more examples in instructions. “Good” and “bad” examples work dramatically better than abstract descriptions of what you want. Show, dont tell. Your GPT is not a mind reader. It’s a pattern matcher, and patterns need examples.

Build once, refine twice, never write that system prompt again. Your future self will quietly thank you.