title: Generating a Low-code Complete Workflow via Task Decomposition and RAG
summary: 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.
paper_authors._id: 67508a4d989680b11105174a
paper_authors.name: Orlando Marquez Ayala
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-12-04T17:06:46.393Z
paper_authors._id: 64820d2bd8662b0714a2a3cd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64820d2bd8662b0714a2a3cd/AHp4bGT05PNlaIGue5gDw.png
paper_authors.fullname: Orlando Marquez
paper_authors.type: user
paper_authors.user: marquezo
paper_authors._id: 67508a4d989680b11105174b
paper_authors.name: Patrice Béchard
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-01-09T14:24:02.932Z
paper_authors._id: 607f060442beb4da0f990182
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/607f060442beb4da0f990182/j5W2tLyU6JqkaTf3kv66s.jpeg
paper_authors.fullname: Patrice Bechard
paper_authors.type: user
paper_authors.user: patricebechard
paper_title: Generating a Low-code Complete Workflow via Task Decomposition and RAG
paper_summary: 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.