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Coze | How to use Workflows

9K views|5 months ago
💫 Short Summary

The video demonstrates creating an AI chatbot with enhanced skills and intelligence using workflows, code nodes, and language models. It focuses on creating a bot with specific knowledge bases and plugins to provide accurate and real-time data, such as NBA game information. By connecting nodes and customizing responses, the video highlights the importance of structuring data efficiently for optimal user experience. Workflows play a crucial role in tailoring responses and enhancing the bot's capabilities, showcasing the versatility and customization options available for users.

✨ Highlights
📊 Transcript
Creating an AI chatbot with persona, extra skills, and intelligence using knowledge bases.
Workflows are introduced to enhance the bot's capabilities in completing multi-step tasks and providing accurate data.
An example of creating an NBA bot to gather game information in real-time is shown.
The importance of workflows in setting up nodes to generate specific answers for users is explained.
Nodes connect to each other to achieve the desired end result.
Using input prompts and code nodes for processing data and generating return values.
Importance of coding knowledge for specific answers from plugins or APIs.
Introduction of knowledge nodes, if conditions, and variable nodes for logic and data storage.
Creating custom plugins to access specific data like game scores and stats in real-time.
Connecting nodes to gather and process information effectively, showcasing the versatility of workflows and plugins in data retrieval and decision-making.
Adding a code node to a workflow for parsing data and customizing responses.
The code node enhances workflow capabilities by extracting specific information efficiently.
Inputs from an NBA Daily data node are used to filter and organize data for output.
The goal is to provide accurate game information and statistics to users.
Structuring data is emphasized for optimal use in delivering information.
Creating a large language model node to enhance user understanding.
Connecting the large language model node to the code node for improved information processing.
Changing the GPT model to GPT 4 for more advanced capabilities.
Customizing the output as game results by referencing input data from games.
Formatting the results for user presentation by adding answer content and utilizing input fields.
The process of passing data through different nodes in a workflow to filter and present specific information.
Utilizing a large language model node to refine data and show only relevant details such as game results, dates, and counts.
Truncating unnecessary data to provide a concise output for users.
Involves inputting data, running it through code nodes, and utilizing a large language model to enhance readability.
Showcasing a customized presentation of game information based on user preferences.
Implementing workflows can enhance bot responses.
Workflows guide bots through different nodes, formatting text and passing inputs as needed.
Utilizing workflows with code nodes and language models can provide accurate information and scores.
Workflows allow for customization and optimization of responses, showcasing platform capabilities.
Viewers are encouraged to check out documentation and join the Discord community for further assistance and updates.