Prompt Template Classification for Responsible AI Prompting
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Solution Overview
Problem
There is a need to guide prompt engineers in generating prompts for generative AI models in a responsible manner, discouraging harmful practices and encouraging improved outcomes, as new models evolve and users lack the necessary skills or domain knowledge.
Innovation Solution
A system that analyzes a plurality of prompts, segments them into invariant and variant portions, classifies the variant portions into categories using a trained classification model, and adjusts or recommends changes to ensure responsible prompting, utilizing a repository of prompt templates for similarity and category matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If prompt engineers use trial and error to generate prompts, then they can eventually achieve desired model output, but the process consumes excessive time and requires advanced domain knowledge that new engineers lack
Solution Approach 1:
The system pre-generates multiple prompt variations using AI models before the user needs them. When a user submits a search query, the system has already created several prompt candidates that can be immediately presented and selected, eliminating the time-consuming trial and error process.
Solution Approach 2:
The system enables users to generate their own prompts through an interactive interface where they can input search queries and receive automatically generated prompt variations. This self-service capability allows users without advanced domain knowledge to create effective prompts independently.
2Manufacturing precision
If prompt engineers manually craft detailed prompts with multiple strategies, then they can achieve improved model output, but the process requires knowledge of generative AI models and subject matter domain that new engineers do not possess
Solution Approach 1:
The system provides self-service prompt generation where users simply input search queries and the AI automatically creates detailed prompts with appropriate strategies, personas, and formatting. Users don't need to know prompt engineering techniques - the system handles all the complexity automatically.
Solution Approach 2:
The system acts as an intermediary between simple user queries and complex model requirements. It translates user intent into structured prompts with persona assignments, formatting specifications, and strategic elements, bridging the gap between user capability and model expectations.
3Adaptability or versatility
If the system generates multiple prompt variations using AI models, then users can select the best prompt, but the system complexity and computational resources increase
Solution Approach 1:
The system segments the prompt generation process into distinct components: user query input, AI-generated prompt variations, user selection interface, and model submission. This modular approach manages complexity by breaking down the overall system into manageable, independent modules.
Solution Approach 2:
The system uses a universal AI model that can generate prompts for multiple different purposes and domains. Rather than having specialized systems for each use case, a single multi-functional model handles diverse prompt generation needs, reducing overall system complexity.
Data Source
AI summary
An embodiment generates, by analyzing prompts, a prompt template. Each prompt includes a text description of a content to be generated by a model. An embodiment classifies, using a first trained classification model, a variant portion of a prompt into a category in a set of categories. An embodiment selects, from a repository of prompt templates including the prompt template, a selected prompt template having a similarity above a threshold similarity to a first prompt. An embodiment classifies, using the selected prompt template, a variant portion of the first prompt into a first category in the set of categories. An embodiment adjusts, responsive to determining that the first category is designated as a harmful category, the variant portion of the first prompt. An embodiment causes, using the adjusted first prompt, the model to produce a first content.