Module 3 · Files, tools and your own data Lesson 10 of 15
The OpenAI API and your first script
OpenAI serves the same models over an API that is billed per token, with a Responses endpoint for text and tool calling, and a separate images endpoint. You do not need this to finish the course, and you do not need it for a fresher job either — but an afternoon here is what turns "I have used ChatGPT" into "I have built with it", which is a different sentence in an interview.
The concept
OpenAI serves the same models over an API that is billed per token, with a Responses endpoint for text and tool calling, and a separate images endpoint. The idea is simple: the same model you have been chatting with also answers a web request, so you can put it inside a script, a spreadsheet or a page. A key identifies you; a request sends the text; a response comes back as data.
The reason to try it once, even if you never build anything: it makes the chat version less mysterious. You see that the whole conversation is text in and text out, that your instructions are literally lines of a request, and that "the model" is one parameter among several.
A first call looks like this:
from openai import OpenAI
client = OpenAI() # reads OPENAI_API_KEY from your environment
r = client.responses.create(
model="gpt-5-mini",
input="In one sentence, what does a data analyst do?",
)
print(r.output_text)Common mistakes
The mistake is pasting a key into a file you then commit. Keys go in environment variables, never in code, and a key that has ever been in a public repository is a key to throw away.
Worked example
Fifty lines of Python that read a CSV of job titles and return a one-line summary of each is a realistic first script — and describes a real productivity gain rather than a demo.
Your practice task
Get a key, run one request that works, and change one word in it to see the answer change. That is the whole of the first afternoon.
Do this before moving on. Reading a solution teaches the shape of it; typing it teaches you.
Key takeaway
OpenAI serves the same models over an API that is billed per token, with a Responses endpoint for text and tool calling, and a separate images endpoint.
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