Vision Capable LLM Steps

Lesson Overview

This video introduces vision capable LLMs, a new development in AI that allows language models to understand and analyze images in addition to text. The presenter demonstrates how to create a workflow in Air Ops that utilizes a vision capable LLM to analyze a screenshot and provide suggestions for improvement.

• 0:00: Introduction to multimodal LLMs and vision capable models
• 0:23: Setting up a workflow to analyze a screenshot using an LLM
• 1:28: Testing the workflow with the Air Ops homepage screenshot
• 1:54: Reviewing the LLM's analysis and suggestions for improvement

Key Concepts

Vision Capable LLMs

Vision capable LLMs are a new development in AI that allows language models to understand and analyze images in addition to text. This multimodal capability enables users to pass images into an LLM and receive insights and suggestions based on the visual content.

Workflow Setup

To utilize vision capable LLMs in Air Ops, you can create a workflow that includes the following steps:

1. Set up an input node with a URL
2. Add a "Screenshot from URL" node to the canvas
3. Add an LLM Step and select a vision capable model (e.g., GPC-40)
4. Connect the output of the screenshot node to the image input of the LLM Step
5. Provide a prompt for the LLM to analyze the screenshot and offer suggestions

Testing and Analysis

Once the workflow is set up, you can test it by providing a URL and running the workflow. The LLM will analyze the screenshot and provide insights and suggestions based on the visual content. You can review the LLM's response to understand how it interprets the image and what improvements it recommends.

Key Takeaways

1. Vision capable LLMs are a significant development in AI, enabling language models to understand and analyze images in addition to text.
2. Air Ops supports vision capable models, allowing users to create workflows that leverage this new capability.
3. To use vision capable LLMs in Air Ops, set up a workflow that includes an input node, a screenshot node, and an LLM Step with a vision capable model.
4. Connect the output of the screenshot node to the image input of the LLM Step and provide a prompt for analysis and suggestions.
5. Test the workflow and review the LLM's response to gain insights into how it interprets the image and what improvements it recommends.