Input Flexibility and Multimodality

Working with the OpenAI Responses API

James Chapman

AI Curriculum Manager, DataCamp

Input Flexibility

Prompts as Strings

response = client.responses.create(
    model="gpt-5-mini"
    input="How old is the Earth?",
    instructions="Answer concisely."
)

input → user inputs
instructions → behavior + requirements

Role-Based Prompts

response = client.responses.create(
    model="gpt-5-mini"
    input=[
        {"role": "system",
         "content": "Answer concisely."},
        {"role": "user",
         "content": "How old is the Earth?"}
    ]
)

Role-Based Messages

response = client.responses.create(
    model="gpt-5-mini"
    input=[
        {"role": "system", "content": "Answer concisely."},

        {"role": "user", "content": "How old is the Sun?"},

        {"role": "assistant", "content": "4.603 billion years"},

        {"role": "user", "content": "How old is the Earth?"}

    ]
)

sys_prompt = "You are a helpful Teacher who provides concise, personalized explanations."
latest_response_id = None 

while True:
    user_input = input("You: ").strip()
    if user_input.lower() == "exit":
        break

    response = client.responses.create(
        model="gpt-5-mini",
        instructions=sys_prompt,
        input=user_input,
        previous_response_id=latest_response_id
    )

    print(f"\nAssistant: {response.output_text}\n")
    latest_response_id = response.id

messages = [{"role": "system"},
            {"content": "You are a helpful Teacher who provides concise, personalized explanations."}]


while True:
    user_input = input("You: ").strip()
    if user_input.lower() == "exit":
        break

    messages.append({"role": "user", "content": user_input})


    response = client.responses.create(
        model="gpt-5-mini",
        input=messages
    )


    print(f"\nAssistant: {response.output_text}\n")
    messages.append({"role": "assistant", "content": response.output_text})

Images in Prompts!

Multi-Modal: interfaced through multiple content modalities

¹ Apple, Inc. Stock Plot from yahoo!finance

Images URLs in Prompts

response = client.responses.create(
    model="gpt-5-mini",
    input=[

        {"role": "user", "content": [
          {"type": "input_text", "text": "Briefly interpret this stock plot."},
          {"type": "input_image", "image_url": "https://...stock_performance.jpg"}]}

    ]
)


print(response.output_text)

Images URLs in Prompts

- Summary: A mild down day - closed at
277.18, down about 0.26% from the previous
close; after-hours ticked slightly lower to
276.94.

¹ Apple, Inc. Stock Plot from yahoo!finance

Images from Local Files

import base64
# Encode binary data as ASCII text characters
with open(image_path, "rb") as f:
    image_base64 = base64.b64encode(f.read()).decode("utf-8")

response = client.responses.create(
    model="gpt-5-mini",
    input=[
        {"role": "user", "content": [
          {"type": "input_text", "text": "Briefly interpret this stock plot."},

          {"type": "input_image", "image_url": f"data:image/jpeg;base64,{image_base64}"}]}

    ]
)

Let's practice!

Working with the OpenAI Responses API