AI in Process Prompt Engineering Playbook

Overview

The business or functional use case for this utility is to accelerate the development and improve the quality of AI-powered process automation within Appian.
It achieves this by:

1. Providing Guidance: Offering developers a standardized playbook with best practices for crafting effective prompts for Appian AI Agents.
2. Demonstrating Practical Applications: Supplying a ready-to-use sample application showcasing how to implement common AI tasks within Appian processes, such as:
   1. Automated Classification and Routing: Categorizing incoming requests (like emails) and routing them to the correct department or process based on their content.
   2. Intelligent Data Extraction: Pulling specific, structured data (like invoice details) from unstructured text for use in downstream processes.
   3. Automated Document Comparison: Identifying and highlighting differences between document versions, crucial for compliance and review processes.
   4. Enhanced User Experience: Automatically highlighting key extracted information within original documents or emails for easier review.

Essentially, the utility helps developers leverage Appian's AI capabilities more efficiently, reducing development time and improving the reliability of AI Agents used in automating business tasks.

Key Features & Functionality

Appian AI Prompt Playbook (Features):

• Standardized Guidance: Provides a structured approach to prompt engineering for Appian AI Agents, aiming for consistency and quality.
• Core Prompting Principles: Outlines fundamental elements for effective prompts, including defining roles, specifying tasks, providing clear steps, structuring information (e.g., using XML tags), defining output formats (like JSON), and using examples.
• Reusable Prompt Patterns: Details specific, reusable prompt structures for common AI tasks:
  
  • Few-Shot Prompting for Classification & Routing (including step-by-step thinking).
  • Zero-Shot Prompting for Extraction & Routing.
  • Detailed prompting for comparing multiple documents and identifying specific changes.
  • Prompting for generating HTML markup to highlight extracted data within text.
• Best Practices: Includes tips for refining prompts, such as handling JSON syntax carefully and applying constraints.

Sample Application (Functionality):

• Runnable Use Cases: Demonstrates the practical implementation of the playbook's patterns within an Appian application context.
• AI in Process Integration: Shows how Appian AI Skills can be configured and integrated into Appian process models to automate tasks.
• Example Implementations: Provides working examples for the four key use cases:
  
  • Classifying an input (e.g., email) and routing based on the classification.
  • Extracting structured data (e.g., invoice details) from text and potentially routing based on extracted values.
  • Executing a document comparison task using AI.
  • Generating an HTML view of an email with extracted entities highlighted.
• Efficiency Driver: Serves as a starting point or template for developers to adapt these AI patterns for their own specific business needs, reducing development effort.