RAG Implementation with Profound AI

Welcome to the final video in our four-part series on getting started with Profound AI! 🎉

In this video, we'll guide you through the steps to create an AI agent and add it to an existing application running in Profound UI. Our goal is to enhance customer service by enabling AI-assisted insights directly within the existing customer service application.

🔹 Key Steps Covered:
0:18 - Introduction and overview of the goal to enhance customer service with AI
0:31 - Creating a new AI agent using the Profound AI IDE
1:11 - Customizing the new agent and saving it within a subdirectory
1:52 - Selecting and configuring the AI model (GPT-4.0 in this example)
2:33 - Setting up the agent's heading, greeting, theme, and brand color
3:34 - Using guidance (prompt engineering) to train the agent
4:38 - Adding and defining variables for dynamic data access
6:12 - Configuring data access for advanced analytics
6:47 - Adding specific database tables and columns for the agent
9:50 - Knowledge documents and routines (covered in future videos)
10:47 - Deploying the agent within the target environment
12:47 - Embedding the agent within the existing Profound UI application
16:19 - Ensuring dynamic data is passed to the agent
17:32 - Testing the embedded agent within the customer service application

By the end of this tutorial, you'll have an AI agent integrated into your customer service application, ready to provide insights and enhance customer interactions.

Be sure to check out Part 4 of the series, where we will delve into more advanced topics and features of Profound AI.

🔗 Profound AI Documentation: https://profoundlogicsupport.atlassia....

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