Opus: A Prompt Intention Framework for Complex Workflow Generation

2025-07-15

AI Summary

To address the insufficient quality and reliability of LLM-generated workflows for complex, multi-intent user queries, this paper proposes Opus, a prompt-based intent framework that introduces a reproducible and customizable intent-capture layer between natural-language queries and workflow generation. Opus decomposes mixed intents into structured intent objects through integrated signal extraction, structured parsing, and intent-driven generation. Its core innovations include formal definitions of “workflow signals” and “structured intents,” along with a lightweight intent abstraction mechanism. Evaluated on a benchmark of 1,000 synthetically generated multi-intent queries, Opus significantly improves the logical coherence, semantic consistency, and semantic similarity of generated workflows—particularly under high-complexity conditions.

Technology Category

• Natural Language Processing: Generation
• Planning, Routing, and Scheduling: Planning with Language Models
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

This paper introduces the Opus Prompt Intention Framework, designed to improve complex Workflow Generation with instruction-tuned Large Language Models (LLMs). We propose an intermediate Intention Capture layer between user queries and Workflow Generation, implementing the Opus Workflow Intention Framework, which consists of extracting Workflow Signals from user queries, interpreting them into structured Workflow Intention objects, and generating Workflows based on these Intentions. Our results show that this layer enables LLMs to produce logical and meaningful outputs that scale reliably as query complexity increases. On a synthetic benchmark of 1,000 multi-intent query-Workflow(s) pairs, applying the Opus Prompt Intention Framework to Workflow Generation yields consistent improvements in semantic Workflow similarity metrics. In this paper, we introduce the Opus Prompt Intention Framework by applying the concepts of Workflow Signal and Workflow Intention to LLM-driven Workflow Generation. We present a reproducible, customizable LLM-based Intention Capture system to extract Workflow Signals and Workflow Intentions from user queries. Finally, we provide empirical evidence that the proposed system significantly improves Workflow Generation quality compared to direct generation from user queries, particularly in cases of Mixed Intention Elicitation.

Problem

• Improving complex workflow generation with LLMs
• Capturing user intentions for structured workflow creation
• Enhancing workflow quality via intention-signal interpretation

Innovation

• Intermediate Intention Capture layer for LLMs
• Structured Workflow Intention objects generation
• Reproducible LLM-based Intention Capture system