ExamplescriptintermediateRunnableguided-flow

Sora Prompting

Runnable example (intermediate) for script using openai, pillow.

Key Facts

Level: intermediate
Runtime: Python • OpenAI API
Pattern: Inspectable flow with visible system boundaries
Interaction: Live sandbox • Script
Updated: 14 March 2026

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Library
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Interaction
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Try the interaction directly in this example’s guided sandbox surface.
Source
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Read the real implementation, highlighted checkpoints, and runtime requirements.
MCP
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Use the same resource inside agents, deterministic exports, and MCP setup flows.

High-level flow

How this example moves from input to execution and reviewable output

Sora Prompting -> Initialize OpenAI client -> Render the visible result -> video

Trigger

Sora Prompting

Runtime

Initialize OpenAI client

Outcome

Render the visible result

Why this page exists

This example is shown as both real source code and a product-facing interaction pattern so learners can connect implementation, UX, and doctrine without leaving the library.

Visual flowReal sourceSandbox or walkthroughMCP access

How should this example be used in the platform?

Use the sandbox to understand the experience pattern first, then inspect the source to see how the product boundary, model boundary, and doctrine boundary are actually implemented.

UX pattern: Inspectable flow with visible system boundaries

Source references

Library entry: models-openai-08-video-4-sora-prompting
Source path: content/example-library/sources/models/openai/08-video/4-sora-prompting.py
Libraries: openai, pillow, pydantic, python-dotenv, requests
Runtime requirements: OPENAI_API_KEY
Related principles: —

4-sora-prompting.py

content/example-library/sources/models/openai/08-video/4-sora-prompting.py

python

from utils.director import SoraDirector
from utils.downloader import download_sora_video
from openai import OpenAI

openai = OpenAI()
director = SoraDirector()
# --------------------------------------------------------------
# Generate a Sora prompt
# --------------------------------------------------------------

prompt = director.generate_sora_prompt(
    "A YouTuber excitedly showing OpenAI's new Sora 2 API release to generate hyper-realistic videos with just a few lines of code."
)
print(prompt)


# --------------------------------------------------------------
# Generate a video
# --------------------------------------------------------------

video = openai.videos.create(
    model="sora-2",
    prompt=prompt,
    size="720x1280",
    seconds="4",
)

print("Video generation started:", video)

# --------------------------------------------------------------
# Download the video
# --------------------------------------------------------------

video = download_sora_video(video=video, output_folder="./output")

What should the learner inspect in the code?

Look for the exact place where system scope is bounded: schema definitions, prompt framing, runtime configuration, and the call site that turns user intent into a concrete model or workflow action.

Look for output contracts and validation

Look for the exact execution call

Look for what the product could expose to the user

How does the sandbox relate to the source?

The sandbox should make the UX legible: what the user sees, what the system is deciding, and how the result becomes reviewable. The source then shows how that behavior is actually implemented.

Read the implementation summary.

Step through the user and system states.

Inspect the source code with the highlighted doctrine decisions in mind.

SandboxInspectable flow with visible system boundaries

Interaction walkthrough

Use the sandbox to step through the user-visible experience, the system work behind it, and the doctrine choice the example is making.

UX explanation

The sandbox explains what the user should see, what the system is doing, and where control or inspectability must remain explicit.

AI design explanation

The page turns raw source into a product-facing pattern: what the model is allowed to decide, what the product should expose, and where deterministic code or review should take over.

Interaction walkthrough

1Read the implementation summary.
2Step through the user and system states.
3Inspect the source code with the highlighted doctrine decisions in mind.

Visible to the user

A script demonstrating openai + pillow.

System work

The product prepares a bounded model or workflow task.

Why it matters

The interface should make the delegated task legible before automation happens.

Used in courses and paths

This example currently stands on its own in the library, but it still connects to the principle system and the broader example family.

Related implementation patterns

Related principles

Runtime architecture

Use this example in your agents

This example is also available through the blueprint’s agent-ready layer. Use the For agents page for the public MCP, deterministic exports, and Claude/Cursor setup.

Define triggers, context, and boundaries before increasing autonomy

Make control, observability, and recovery explicit in the runtime

Choose the right operational patterns before delegating to workflows

Open For agents