Prompt Compiler Using Templates and Fragments for Megaprompt Assembly

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Solution Overview

Problem

Conventional prompt engineering for large language models is cumbersome and inefficient, particularly when composing megaprompts, and users lack effective strategies to reliably elicit specific behaviors from these models.

Innovation Solution

A computing system that includes a prompt library with fragments and templates, which programmatically constructs prompts using fragment and template selection logic, incorporating domain-specific, few-shot task examples, retrieval-augmented generation, and multimodal data, to generate optimized prompts for machine learning models.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual prompt engineering is used to construct megaprompts, then users can customize prompts for specific tasks, but the process becomes cumbersome and time-consuming

Engineering Contradiction:

Improveease of prompt constructionVSAvoidtime required for prompt engineering

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The patent segments prompts into reusable fragments and templates stored in a library. Instead of manually constructing entire prompts from scratch, users can select and combine pre-defined fragments (e.g., role definitions, task descriptions, constraints) with templates to assemble megaprompts automatically, significantly reducing construction time and effort

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary action by pre-creating and storing optimized prompt fragments and templates in a library before actual use. These pre-computed prompts are ready to be retrieved and assembled, eliminating the need for users to perform time-consuming prompt engineering manually during task execution

Inventive Principle:

Principle #10Preliminary action

2Reliability

If users manually construct prompts, then they can control prompt structure, but they lack effective strategies to reliably elicit specific model behaviors

Engineering Contradiction:

Improvereliability of model behaviorVSAvoidcomplexity of prompt construction

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system incorporates feedback mechanisms where the library is populated with prompt fragments and templates that have been pre-validated for reliability. Users can observe which fragments and templates produce desired model behaviors, and the system can learn from usage patterns to refine future prompt assemblies, improving reliability through iterative feedback

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent changes parameters by providing users with controlled variables in the form of standardized prompt fragments (e.g., different role definitions, task types, constraint levels). Users can adjust these parameters by selecting different fragments without needing to understand complex prompt engineering, making reliable behavior elicitation accessible while reducing construction complexity

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If the prompt library includes diverse prompt fragments and templates, then the system becomes more versatile, but the system complexity increases

Engineering Contradiction:

Improveversatility of prompt constructionVSAvoidcomplexity of prompt library management

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies universality by designing a unified library structure that stores multiple types of prompt fragments (role definitions, task descriptions, constraints, examples) and templates in a consistent format. This universal structure allows the same retrieval and assembly mechanisms to handle diverse prompt requirements across different domains and tasks, increasing versatility without proportionally increasing complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system uses copying by storing standardized prompt fragments and templates as reusable copies in the library. Instead of creating unique prompts for each task, the system copies and combines existing validated fragments, reducing the cognitive load on users while maintaining versatility across different applications

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20260004072A1Prompt compiler

Publication Date: 2026.01.01 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

A computing system including memory storing a prompt library. The prompt library includes prompt fragments and prompt templates. The computing system further includes one or more processing devices configured to, at a prompt compiler, receive a prompt generation input including prompt input data. At the prompt compiler, based at least in part on the prompt input data, the one or more processing devices are further configured to select a prompt template and one or more of the prompt fragments from the prompt library. The one or more processing devices are further configured to fill the selected prompt template with the prompt input data and the one or more selected prompt fragments to compute a compiled prompt. At a first machine learning model, the one or more processing devices are further configured to process the compiled prompt and to output the machine learning model output.