Video Models

• Key points are in the visual diagram above.

Real-world tasks involve multiple data types: analyze a chart in a PDF, describe a video, answer questions about a photo. Multimodal models handle all inputs in one model, eliminating brittle multi-model pipelines.

A doctor who can listen to symptoms (audio), read lab results (text), examine X-rays (images), and watch patient movement (video) — all integrated into one diagnosis, not four separate specialists.

Scenario

Product support agent: customer sends screenshot of error + describes problem in text. GPT-4o analyzes screenshot (reads error message, identifies UI state) combined with text description → provides targeted fix. Single model, no separate OCR + LLM pipeline.

Solution

In Model Engineering (Awareness), apply Video Models to this scenario: Product support agent: customer sends screenshot of error + describes problem in text. 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 Video Models (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 Video Models happens in the code.

1from openai import OpenAI  # import dependencies2import base64  # import dependencies3
4client = OpenAI()  # create API client5
6# Multimodal: text + image + structured request7response = client.chat.completions.create(  # call the API8    model="gpt-4o",9    messages=[{10        "role": "user",11        "content": [12            {"type": "text", "text": "Analyze this dashboard screenshot. What trends do you see? Any anomalies?"},13            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{base64.b64encode(open('dashboard.png','rb').read()).decode()}"}},14        ],15    }],16)17
18# Gemini multimodal: text + video19# import google.generativeai as genai20# model = genai.GenerativeModel("gemini-2.0-flash")21# response = model.generate_content(["Summarize this video:", video_file])

• Using separate OCR + LLM when native multimodal suffices
• Sending full-resolution images — expensive and unnecessary
• Not accounting for multimodal token costs in pricing
• Assuming all models handle all modalities equally

Quick recap — the most important points from this module.