# Generating a Low-code Complete Workflow via Task Decomposition and RAG arXiv: https://arxiv.org/abs/2412.00239 ยท HTML: https://mlsanity.com/paper/2412.00239/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 54 references that are in the map. - First posted on arXiv 29 Nov 2024. ## Builds on (most cited first, 29 of 29) - [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023) - [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023) - [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023) - [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021) - [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024) - [Large Dual Encoders Are Generalizable Retrievers](https://mlsanity.com/paper/2112.07899/) (2021) - [RAFT: Adapting Language Model to Domain Specific RAG](https://mlsanity.com/paper/2403.10131/) (2024) - [Seven Failure Points When Engineering a Retrieval Augmented Generation System](https://mlsanity.com/paper/2401.05856/) (2024) - [Reducing hallucination in structured outputs via Retrieval-Augmented Generation](https://mlsanity.com/paper/2404.08189/) (2024) - [Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks](https://mlsanity.com/paper/2304.14732/) (2023) - [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021) - [KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases](https://mlsanity.com/paper/2308.11761/) (2023) - [JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability](https://mlsanity.com/paper/2402.17887/) (2024) - [Speech-Copilot: Leveraging Large Language Models for Speech Processing via Task Decomposition, Modularization, and Program Generation](https://mlsanity.com/paper/2407.09886/) (2024) - [Studying Software Engineering Patterns for Designing Machine Learning Systems](https://mlsanity.com/paper/1910.04736/) (2019) - [Towards Uncovering How Large Language Model Works: An Explainability Perspective](https://mlsanity.com/paper/2402.10688/) (2024) - [The Dark Side of Function Calling: Pathways to Jailbreaking Large Language Models](https://mlsanity.com/paper/2407.17915/) (2024) - [Quantifying the Capabilities of LLMs across Scale and Precision](https://mlsanity.com/paper/2405.03146/) (2024) - [The Challenges of Evaluating LLM Applications: An Analysis of Automated, Human, and LLM-Based Approaches](https://mlsanity.com/paper/2406.03339/) (2024) - [Low-code LLM: Graphical User Interface over Large Language Models](https://mlsanity.com/paper/2304.08103/) (2023) - [Finding the Right Moment: Human-Assisted Trailer Creation via Task Composition](https://mlsanity.com/paper/2111.08774/) (2021) - [Adapting Software Architectures to Machine Learning Challenges](https://mlsanity.com/paper/2105.12422/) (2021) - [A Jailbroken GenAI Model Can Cause Substantial Harm: GenAI-powered Applications are Vulnerable to PromptWares](https://mlsanity.com/paper/2408.05061/) (2024) - [A Taxonomy of Foundation Model based Systems through the Lens of Software Architecture](https://mlsanity.com/paper/2305.05352/) (2023) - [TaskGen: A Task-Based, Memory-Infused Agentic Framework using StrictJSON](https://mlsanity.com/paper/2407.15734/) (2024) - [Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository](https://mlsanity.com/paper/2303.13173/) (2023) - [Investigating the Impact of SOLID Design Principles on Machine Learning Code Understanding](https://mlsanity.com/paper/2402.05337/) (2024) - [Architecture Decisions in AI-based Systems Development: An Empirical Study](https://mlsanity.com/paper/2212.13866/) (2022) - [Learning Abstract Visual Reasoning via Task Decomposition: A Case Study in Raven Progressive Matrices](https://mlsanity.com/paper/2308.06528/) (2023) ## Built on by (most cited first, 3 of 3) - [Terminal Agents Suffice for Enterprise Automation](https://mlsanity.com/paper/2604.00073/) (2026) - [StarFlow: Generating Structured Workflow Outputs From Sketch Images](https://mlsanity.com/paper/2503.21889/) (2025) - [ReusStdFlow: A Standardized Reusability Framework for Dynamic Workflow Construction in Agentic AI](https://mlsanity.com/paper/2602.14922/) (2026) ## Abstract (arXiv) AI technologies are moving rapidly from research to production. With the popularity of Foundation Models (FMs) that generate text, images, and video, AI-based systems are increasing their complexity. Compared to traditional AI-based software, systems employing FMs, or GenAI-based systems, are more difficult to design due to their scale and versatility. This makes it necessary to document best practices, known as design patterns in software engineering, that can be used across GenAI applications. Our first contribution is to formalize two techniques, Task Decomposition and Retrieval-Augmented Generation (RAG), as design patterns for GenAI-based systems. We discuss their trade-offs in terms of software quality attributes and comment on alternative approaches. We recommend to AI practitioners to consider these techniques not only from a scientific perspective but also from the standpoint of desired engineering properties such as flexibility, maintainability, safety, and security. As a second contribution, we describe our industry experience applying Task Decomposition and RAG to build a complex real-world GenAI application for enterprise users: Workflow Generation. The task of generating workflows entails generating a specific plan using data from the system environment, taking as input a user requirement. As these two patterns affect the entire AI development cycle, we explain how they impacted the dataset creation, model training, model evaluation, and deployment phases.