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Intelligent Prompt Generation and Classification Automation Workflow

This workflow automatically generates and classifies prompts by utilizing the Google Gemini language model to process user input, producing high-quality structured prompt text. It intelligently names and categorizes the prompts, ultimately saving the results to an Airtable database. This process streamlines traditional manual editing, enhances the efficiency of building and maintaining the prompt library, and ensures that the content is accurate and standardized. It is suitable for various scenarios such as AI product development and automated customer service, improving the effectiveness and consistency of AI agent task execution.

Workflow Diagram
Intelligent Prompt Generation and Classification Automation Workflow Workflow diagram

Workflow Name

Intelligent Prompt Generation and Classification Automation Workflow

Key Features and Highlights

This workflow leverages the Google Gemini (PaLM) language model to automatically receive user chat inputs, intelligently generate structured prompt content, and perform automatic naming and classification of prompts. The results are then synchronized and saved to an Airtable database. The workflow incorporates multi-level output parsers to ensure the generated content is accurate and formatted according to standards. Detailed role definitions and operational instructions further enhance the practicality and execution efficiency of the prompts.

Core Problems Addressed

Traditional prompt generation often relies on manual editing, which is inefficient and lacks unified classification management. This workflow automates prompt creation, structured parsing, and classification, significantly simplifying the construction and maintenance of prompt libraries. It prevents content disorder and duplication, thereby improving the accuracy and consistency of AI agent task execution.

Application Scenarios

  • Rapid construction and management of prompt libraries by AI product development teams
  • Prompt design and optimization for AI agents such as automated customer service and intelligent assistants
  • Enterprises and developers requiring continuous iteration and classification of prompts to enhance model response quality
  • Any application scenario needing automatic generation and management of text templates via chat interfaces

Main Process Steps

  1. Chat Message Trigger: Monitor and capture user chat inputs.
  2. Generate New Prompt: Invoke the Google Gemini model to generate high-quality prompt text based on predefined roles and task rules.
  3. Edit Fields: Organize and format the generated prompt content.
  4. Automatic Output Correction: Use an auto-correction parser to improve the accuracy and standardization of the generated content.
  5. Structured Parsing: Parse the prompt generation results into a structured JSON format, including name and classification information.
  6. Classification and Naming: Utilize the language model to intelligently classify prompts and automatically assign names.
  7. Set Prompt Fields: Prepare prompt data fields for saving, including name, classification, and content.
  8. Save to Airtable: Write the organized prompt information into Airtable tables for centralized prompt library management.
  9. Return Results: Output the final generated prompt text for subsequent use or display.

Involved Systems or Services

  • Google Gemini (PaLM) API: A powerful language generation and understanding service used for prompt generation and classification.
  • Airtable: A cloud-based database for storing and managing the prompt library.
  • n8n Platform Built-in Nodes: Including webhook triggers, field setting nodes, output parsers, etc., to orchestrate workflows and handle data processing.

Target Users and Value

  • AI Developers and Data Scientists: Quickly build and manage diverse prompt sets to improve AI model invocation efficiency.
  • Product Managers and Operations Personnel: Easily maintain prompt libraries to ensure AI output quality and consistency in business scenarios.
  • Automation Engineers: Implement complex prompt generation and classification logic via low-code solutions, reducing development costs.
  • Any teams requiring automatic generation and management of text templates through natural language interfaces, enhancing standardization and automation of content generation.