Prompt Templates for Secure LLM Context Integration
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Solution Overview
Problem
Current systems lack user-friendly and secure methods for integrating Large Language Models (LLMs) into business operations, requiring manual entry of contextual data which poses privacy and security risks.
Innovation Solution
A declarative prompt creation and management tool that embeds reusable, customizable prompt templates into the workflow, allowing users to generate prompts without specialized knowledge, ensuring data security and integration with AI systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual entry of contextual data is used to prompt LLMs, then the LLM can produce responsive action, but privacy and security risks increase
Solution Approach 1:
The patent introduces prompt templates as an intermediary layer between the user and the LLM. These templates contain pre-defined structures with placeholders for contextual data, allowing users to input information securely without directly exposing sensitive data to the LLM. The template system mediates the interaction by structuring the prompt in a controlled manner that protects privacy while maintaining LLM functionality.
2Reliability
If prompt engineering is performed manually by specialists, then LLM can generate desired responses, but the system complexity and operational difficulty increase
Solution Approach 1:
The patent enables users to create and customize their own prompt templates without requiring specialized prompt engineering knowledge. The system provides tools for users to independently define template structures, select placeholders, and configure prompt parameters. This self-service capability allows non-experts to generate effective prompts by following guided processes rather than relying on specialized personnel.
Solution Approach 2:
The patent breaks down the complex task of prompt engineering into manageable components through templates. Instead of requiring users to craft entire prompts from scratch, the system segments the prompt into a fixed template structure with variable placeholders. Users only need to fill in specific data points rather than designing the entire prompt architecture, significantly reducing operational complexity.
3Reliability
If contextual data is integrated into LLM prompts, then the LLM can comprehend and generate relevant text, but the difficulty of adding context without manual entry increases
Solution Approach 1:
The patent prepares prompt templates in advance with pre-defined structures, placeholders, and formatting. This preliminary action involves creating reusable template frameworks that already contain the necessary contextual structure before actual use. By pre-configuring the template architecture, the system eliminates the need for complex real-time context integration, as users simply need to populate pre-identified placeholders with relevant data.
Data Source
AI summary
Disclosed herein are system, method, and computer program product aspects for generating a prompt for an LLM using a prompt template. A system retrieves an external data reference to context data included in a prompt template. The prompt template is selected from a plurality of prompt templates based on the field type of an input field in an interface. The system then generates a prompt based on the prompt template and the retrieved context data. The system then prompts the LLM accordingly, which generates a non-deterministic output for responding to the user request.