Image Generation

• Key points are in the visual diagram above.

Multimodal agents may need to generate diagrams, mockups, or visual content — not just analyze images.

Image generation is describing a painting to an artist who paints it instantly — quality depends on how specific your description is.

Scenario

Marketing agent generates 3 ad banner variants from product description, user picks one for campaign.

Solution

In Model Engineering (Awareness), apply Image Generation to this scenario: Marketing agent generates 3 ad banner variants from product description, user picks one for campaign. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Image Generation (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Image Generation happens in the code.

1# Image Generation — minimal example2from openai import OpenAI3
4client = OpenAI()  # create API client5
6# Ask the model to explain this topic7response = client.chat.completions.create(  # core API call for Image Generation8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain image generation clearly."},11        {"role": "user", "content": f"What is image generation?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Treating Image Generation as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for image generation

Quick recap — the most important points from this module.