# What custom gpts are and the GPT store
A custom GPT is a configured version of ChatGPT that you set up once for a job you do over and over, then reuse (or publish) without rebuilding the setup every time. Think of it as ChatGPT plus a fixed system prompt, a set of files, optional tool connections, and a name, all bundled into a shareable unit that lives in the GPT Store.
You already know how to write a good prompt. A custom GPT is what you reachreachThe number of unique people exposed to your message in a given period. Unlike impressions, reach counts each person once, no matter how often they see it.View full definition → for when one good prompt is not enough: when the instructions are long, when the same reference files matter every time, or when other people need to run the job without seeing the machinery.
Open the builder at chatgpt.com/gpts/editor (Plus, Pro, Team, or Enterprise). A custom GPT is made of a few parts:
The builder has a conversational setup mode, but for anything serious you should edit the Configure tab directly. The conversational mode is convenient and vague; the Configure tab is where you control exactly what the instructions say.
Say your company has a tone guide: warm, direct, no hype, British spelling, never use the word "leverage." Marketing, sales, and support all need to write in that voice. Pasting the guide into every chat is brittle and inconsistent.
Build a "Acme Brand Voice Writer" GPT instead.
Instructions (the contract):
You are Acme's brand voice writer. Rewrite or draft copy in Acme's
voice: warm, direct, plain. No hype words (leverage, synergy,
game-changing, unlock). British spelling. Sentences under 25 words
where possible. Active voice.
When the user gives raw copy, return: (1) the rewritten version,
(2) a one-line note on what you changed and why.
If the request conflicts with the tone guide in your knowledge files,
follow the tone guide and say so briefly.Knowledge: upload acme-tone-guide.pdf and a dos-and-donts.md. Now every conversation starts with the full guide loaded, no pasting.
Capabilities: turn Web Search off (you do not want it inventing facts about products), keep Canvas on so writers can edit drafts side by side.
Conversation starters: "Rewrite this homepage hero," "Draft a launch email," "Tighten this paragraph."
Now anyone on the team opens one GPT and gets consistent output. That consistency is the whole point: the instructions and the tone guide do not drift between people or sessions.
You can save prompts as text snippets, and ChatGPT Projects let you set custom instructions plus shared files scoped to a workspace. So when is a full custom GPT the right tool? Use this decision lens.
Reach for a custom GPT when:
A saved prompt or Project is enough when:
Rough rule: Projects are for *your* recurring work with shared files; custom GPTs are for *packaging a repeatable job for others* (or for a clean, named tool you return to). They overlap, and that is fine.
A GPT Action lets your custom GPT call an external APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → during a conversation. This is the same idea as function calling on the APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition →, but configured through an OpenAPI schemaschemaA schema is the formal blueprint that defines how data is structured, named, typed, and related within a database, file, or message.View full definition → in the builder instead of code.
Extend the brand voice writer: suppose approved product facts live in an internal endpoint. You give the GPT an Action so it can fetch real specs instead of guessing. You define the action with an OpenAPI schemaschemaA schema is the formal blueprint that defines how data is structured, named, typed, and related within a database, file, or message.View full definition → like this:
openapi: 3.1.0
info:
title: Acme Product Facts
version: "1.0"
servers:
- url: https://api.acme.com
paths:
/products/{sku}:
get:
operationId: getProductFacts
summary: Get approved facts for a product SKU
parameters:
- name: sku
in: path
required: true
schema: { type: string }
responses:
"200":
description: Product facts
content:
application/json:
schema:
type: object
properties:
name: { type: string }
claims: { type: array, items: { type: string } }Paste that into the Action editor, add an auth method (APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → key or OAuth), and the GPT can now call getProductFacts when a user asks it to write copy for a SKU. The model decides when to call it based on your instructions and the action's summary. See Actions in the docs for auth and schemaschemaA schema is the formal blueprint that defines how data is structured, named, typed, and related within a database, file, or message.View full definition → details.
Two things to internalize:
Do not confuse Actions with Connectors. Connectors link ChatGPT to data sources like Google Drive, SharePoint, or GitHub for retrieval inside chats and Projects. Actions are custom APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → calls you wire into a specific GPT. Use Connectors to bring in your documents; use Actions to hit a custom endpoint or trigger an operation.
The GPT Store is where published custom GPTs are discovered. When you finish building, you choose visibility:
To publish publicly you need a verified builder profile (a name or a verified domain). The Store organizes GPTs into categories and surfaces popular ones. OpenAI has run a revenue program for US builders based on usage; treat specifics as subject to change and check current terms rather than assuming a fixed rate. The official overview is at help.openai.com.
One sharp limitation to remember: a public GPT runs on the *user's* ChatGPT account and plan. Free users may hit different model access than you did while building. Test as a non-builder before you ship.
Knowledge check
1. According to the lesson, what is the best description of a custom GPT?
2. Why does the lesson recommend editing the Configure tab directly rather than using the conversational setup mode?
3. In ChatGPT, what is the typical role of a 'system prompt' like the Instructions field of a custom GPT?
4. Select ALL correct answers about situations where the lesson says you should reach for a custom GPT instead of a single good prompt.
Sélectionnez toutes les réponses correctes.
5. Select ALL correct answers that are listed as parts (anatomy) of a custom GPT in the lesson.
Sélectionnez toutes les réponses correctes.
This is the distinction that separates people who *use* ChatGPT from people who *build products* on OpenAI.
A custom GPT lives inside ChatGPT. It is bounded by the ChatGPT app: the user must have a ChatGPT account, the UI is fixed, and you configure it with instructions, files, and Actions. No code required.
The OpenAI API (the Responses API and the older Chat Completions APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition →) is for embeddingembeddingAn embedding is a numerical vector that represents data (text, images, or items) in a way that captures meaning, so similar items sit close together in space.View full definition → the model in *your own* application. You write code, you control the interface, you pay per tokentokenA token is the basic unit of text that language models process, often a word fragment, whole word, or punctuation mark rather than a single character.View full definition →, and you can use function calling, structured outputs, the Agents SDK, and more.
The same brand voice job, on the APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → side, looks like this:
from openai import OpenAI
client = OpenAI()
TONE_GUIDE = open("acme-tone-guide.md").read()
resp = client.responses.create(
model="gpt-4.1",
instructions=f"Rewrite copy in Acme's brand voice.\n\n{TONE_GUIDE}",
input="Rewrite: 'We leverage cutting-edge synergy to unlock value.'",
)
print(resp.output_text)Same instructions, same tone guide. The difference is *where it runs*: inside ChatGPT for your team, versus inside your own app for your customers.
How to choose:
A common path: prototype the behavior as a custom GPT to validate the instructions quickly, then port the proven instructions into an APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition → integration when you need scale or a custom interface.
A published GPT is a small product. Treat it that way.
operationId and summary fields in an OpenAPI schemaschemaA schema is the formal blueprint that defines how data is structured, named, typed, and related within a database, file, or message.View full definition →) when the GPT needs live external data; use Connectors to pull in documents.