How to build your own GPT agent

TL;DR

Use the “Create” tab to get a working GPT quickly, but treat it as a starting point with limited control.

Briefing Cornell Notes

Briefing

OpenAI’s GPT Builder is positioned as a no-code path to creating custom AI agents—then upgrading them into higher-performing “GPTs” by adding tailored instructions, private knowledge files, and (optionally) external tool integrations. The practical takeaway is that the biggest competitive edge doesn’t come from picking a popular persona; it comes from writing strong custom instructions and grounding the agent in uploaded source material, so it can deliver specific, usable advice rather than generic chat.

The walkthrough starts with the simplest route: clicking “Create a GPT” and letting the builder generate a working agent from plain-English prompts. From there, the builder offers a “Configure” tab for deeper control—especially “custom instructions,” which the creator treats as the most important lever. To demonstrate, a “Robert Green” advice GPT is built with a brutally honest, direct tone, and conversation starters aimed at personal development and decision-making. The builder also generates a profile picture (via DALL·E), but the icon choice is framed as secondary; once the GPT Store opens, distinct branding matters more than the initial auto-generated image.

A key limitation appears early: the “Create” tab is capped in what it can do, so it’s labeled “level one.” The intermediate “level two” comes from “Configure,” where the agent’s behavior is shaped through custom instructions and knowledge. The creator tests the GPT and finds responses can be too long and vague, then iterates by tightening instructions—specifically asking for concise, clear answers that don’t waste the user’s time. The process highlights a common failure mode: if instructions are sloppy or incomplete, the GPT underperforms even when the persona is compelling.

The most consequential upgrade is “Knowledge.” Instead of relying solely on training data, the agent is given Robert Green’s books as PDFs, enabling it to cite relevant chapters and tailor advice to the user’s situation. The walkthrough also notes operational friction: OpenAI service outages prevent reliable use of the “upload files” button, so PDFs are added via drag-and-drop. Despite the instability, the workflow demonstrates how the GPT can search within uploaded documents and produce grounded recommendations.

Finally, the “level three” upgrade is “Add actions,” which turns a GPT into an agent that can call external APIs and connect to tools—illustrated with Zapier-style integrations that can reach thousands of apps. The creator argues that this is where truly standout GPTs are built, such as automation assistants for email, task management, or Slack.

Overall, the guidance is blunt: profile pictures and conversation starters are minor. Competitive advantage comes from engineered custom instructions and curated knowledge, with actions reserved for the most advanced, tool-using agents. The timing is also framed as urgent—because GPTs that attract large audiences could materially change a creator’s prospects, especially with OpenAI’s revenue-sharing promise for popular GPTs.

Cornell Notes

The walkthrough lays out a three-level path for building a custom GPT agent: start with the no-code “Create” tab, upgrade behavior in “Configure,” and then add tool use in “Add actions.” The biggest performance gains come from (1) writing strong custom instructions that control tone, structure, and concision, and (2) grounding the agent with uploaded “Knowledge” files so answers reference specific books and chapters. A Robert Green advice GPT is used as the example, with iterative testing to reduce rambling and increase specificity. The “Add actions” stage is presented as the route to advanced agents that can call external APIs and automate real workflows. Operational outages can disrupt uploading, but drag-and-drop knowledge ingestion still works in practice.

Why does the walkthrough treat “custom instructions” as the most important part of building a GPT?

Because they directly shape the agent’s behavior—tone, response length, and how it handles user requests. In the example, the initial output is overly long and vague, so the creator revises the instructions to demand concise, clear answers that don’t waste the user’s time. That change immediately improves readability. The walkthrough also warns that if custom instructions are “messed up,” the GPT can underperform even if the persona and knowledge are good.

What role does “Knowledge” play compared with relying on training data alone?

“Knowledge” lets the GPT use uploaded documents (like PDFs) that aren’t limited to what it already learned during training. In the Robert Green example, the creator uploads multiple Robert Green books and then instructs the GPT to select relevant books and chapters, read them, and cite which chapters were used. This turns generic advice into grounded recommendations tied to specific sources.

How does the walkthrough’s “three levels” framework map to the GPT Builder interface?

Level one uses the “Create” tab for a quick, beginner-friendly GPT that works with minimal effort. Level two uses the “Configure” tab for fine control—especially custom instructions and knowledge uploads. Level three uses “Add actions” to connect external APIs and tools, enabling automation and real-world task execution beyond text-only responses.

What does the creator mean by “competitive advantage” in the GPT Store?

The walkthrough argues that easy GPTs get copied, so they struggle to stand out. To reach top placement, creators need differentiated behavior and grounded outputs—primarily through custom instructions and curated knowledge. The creator contrasts the limited “Create” tab with the more powerful “Configure” tab, emphasizing that most users won’t invest the time to write strong instructions.

Why are “actions” framed as the path to the best GPTs?

Actions allow a GPT to call functions and connect to outside services via APIs. The walkthrough gives the example of integrating with Zapier-style connectivity to thousands of apps, which enables advanced assistants like email management, task/todo workflows, or Slack-related automation—capabilities that go beyond answering questions.

What practical issues can derail building a GPT, and how does the walkthrough respond?

OpenAI outages can break parts of the workflow—especially uploading files through the “upload files” button. The creator adapts by using drag-and-drop to add PDFs into Knowledge and continues testing. The walkthrough also notes that profile picture generation may fail or revert to a default icon, so branding should be treated as secondary to instructions and knowledge.

Review Questions

When would you choose the “Create” tab versus “Configure,” and what specific improvements do you expect from each?
How would you rewrite custom instructions to reduce overly long or vague responses in a GPT?
What steps would you include in an instruction set to ensure the GPT cites specific chapters from uploaded PDFs?

Key Points

1
Use the “Create” tab to get a working GPT quickly, but treat it as a starting point with limited control.
2
Write custom instructions carefully; they govern tone, structure, and concision, and poor instructions can ruin output quality.
3
Add “Knowledge” by uploading relevant PDFs so answers can reference specific books and chapters instead of staying generic.
4
For advanced capability, use “Add actions” to connect external APIs and automate workflows rather than only generating text.
5
Profile pictures and conversation starters matter less than instructions and knowledge for performance and differentiation.
6
Plan for operational hiccups: outages can disrupt file upload, so alternative ingestion methods (like drag-and-drop) may be necessary.

Highlights

The biggest upgrade from a basic GPT comes from “Configure,” especially custom instructions and “Knowledge,” not from the initial persona or auto-generated icon.

Tightening custom instructions to demand concise, clear responses can quickly fix rambling outputs.

Uploading PDFs into Knowledge enables grounded advice that can cite specific chapters, turning generic guidance into source-based recommendations.

“Add actions” is the gateway to agentic GPTs that can call external APIs and integrate with many tools for real automation.

Topics

GPT Builder
Custom Instructions
Knowledge Uploads
Actions and APIs
Prompt Engineering