Calling the OpenAI API

OpenAI ships chat.completions (classic) and responses (modern). New code should prefer responses — it unifies text, tools, and structured output.

from openai import OpenAI

client = OpenAI()

def ask(prompt: str) -> str:
    try:
        r = client.responses.create(
            model="gpt-5",
            input=[
                {"role": "system", "content": "Be concise."},
                {"role": "user",   "content": prompt},
            ],
        )
        return r.output_text
    except Exception as e:
        print(f"OpenAI call failed: {e}")
        raise

print(ask("Explain recursion in one sentence."))

Streaming

def ask_stream(prompt: str) -> None:
    with client.responses.stream(
        model="gpt-5",
        input=[{"role": "user", "content": prompt}],
    ) as stream:
        for event in stream:
            if event.type == "response.output_text.delta":
                print(event.delta, end="", flush=True)
        stream.until_done()
    print()

Understanding "Calling the OpenAI API" in practice: AI-assisted coding shifts work from syntax recall to design thinking — models handle boilerplate so you focus on architecture. The Responses API is OpenAI's modern surface. One call, text and tools. Learn the shape you'll use most — and knowing how to apply this gives you a concrete advantage.

• Apply Responses API in your ai-coding workflow to get better results
• Apply chat completions in your ai-coding workflow to get better results
• Apply streaming in your ai-coding workflow to get better results
• Apply model in your ai-coding workflow to get better results

1. 1Use AI to generate unit tests for an existing function
2. 2Ask AI to refactor a messy function and explain the changes
3. 3Have AI suggest a code review for a recent pull request

The big idea: responses.create for the modern path, stream for UIs, and centralize model ids so provider swaps are painless.