Markup Language Interface for Generative Model Prompting

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Solution Overview

Problem

Users face difficulties in crafting effective prompts for large language models due to the unintuitive nature of prompt crafting, which can lead to undesirable outputs, especially for non-experts who lack the necessary knowledge and structure.

Innovation Solution

A specialized markup language interface and integrated development environment that processes user input to determine intent and generate refined prompts, incorporating delimiters, text-encoding systems, and prompt term suggestions to ensure accurate interpretation by the generative model.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If users craft prompts without a particular structure and terminology, then the prompt crafting process is simpler and more intuitive, but the outputs do not reflect the intent of the user

Engineering Contradiction:

Improveprompt crafting simplicityVSAvoidoutput accuracy

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The patent introduces an intermediary system that includes a markup language parser and prompt generator. This intermediary takes simple user input and automatically transforms it into structured prompts with appropriate terminology, weighting, and formatting. The intermediary resolves the contradiction by shielding users from complexity while ensuring output accuracy through automated structuring and term insertion.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary actions by pre-defining prompt structures, terminology sets, and weighting schemes. Before users need to craft prompts, the system has already prepared templates and common term combinations. This preliminary preparation allows users to simply select or input basic requirements while the system handles the complex structuring and term selection in advance.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If users craft prompts with proper structure and terminology, then the outputs better reflect user intent, but the prompt crafting process becomes more difficult and requires expert knowledge

Engineering Contradiction:

Improveoutput accuracyVSAvoidprompt crafting difficulty

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system enables self-service by automatically generating structured prompts from minimal user input. Users don't need to manually construct complex prompt structures or select appropriate terminology - the system serves itself by autonomously organizing the prompt elements, inserting technical terms, and applying proper weighting based on the user's simple requirements.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The intermediary system acts as a bridge between simple user intent and complex prompt requirements. It translates high-level user requirements into detailed structured prompts with proper terminology, automatically handling the knowledge gap without requiring users to learn complex prompt engineering concepts.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If deterministic weighting is applied to prompt terms, then the generative model outputs are more consistent with user intent, but the system complexity increases

Engineering Contradiction:

Improveoutput consistencyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system manages complexity by parameterizing the weighting system. Instead of hardcoding complex weighting logic, the system uses configurable parameters and metadata associated with prompt terms. This allows deterministic weighting to be implemented through simple parameter adjustments rather than complex system architecture changes.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system segments the weighting complexity into manageable components: term frequency analysis, metadata-based weighting, and configurable priority levels. By dividing the weighting system into separate, modular components, the overall system complexity is reduced while maintaining deterministic output consistency.

Inventive Principle:

Principle #1Segmentation

4Reliability

If comprehensive prompt term suggestions are provided to users, then users can create more accurate prompts, but the user interface complexity and computational resources increase

Engineering Contradiction:

Improveprompt accuracyVSAvoidinterface complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system applies partial action by providing prompt term suggestions selectively rather than comprehensively. It analyzes user input and only suggests terms that are relevant to the specific context, rather than displaying all possible terms. This reduces interface complexity and computational resources while maintaining prompt accuracy through targeted suggestions.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20240311652A1Markup Language for Generative Model Prompting

Publication Date: 2024.09.19 GOOGLE LLC

AI summary

Systems and methods for prompt generation for generative models can include utilizing a specialized markup language. A markup language transform can be utilized to augment user input data to generate a prompt that includes structure and/or wording that facilitates the generation of a generative output that reflects a user's intent. The systems and methods can leverage the specialized markup language and/or an integrated development environment interface to inform a user of the prompt parts and provide editing options.