Generating a Low-code Complete Workflow via Task Decomposition and RAG
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.
67508a4d989680b11105174a
Orlando Marquez Ayala
extracted_confirmed
2024-12-04T17:06:46.393Z
64820d2bd8662b0714a2a3cd
https://cdn-avatars.huggingface.co/v1/production/uploads/64820d2bd8662b0714a2a3cd/AHp4bGT05PNlaIGue5gDw.png
Orlando Marquez
user
marquezo
67508a4d989680b11105174b
Patrice Béchard
extracted_confirmed
2025-01-09T14:24:02.932Z
607f060442beb4da0f990182
https://cdn-avatars.huggingface.co/v1/production/uploads/607f060442beb4da0f990182/j5W2tLyU6JqkaTf3kv66s.jpeg
Patrice Bechard
user
patricebechard
Generating a Low-code Complete Workflow via Task Decomposition and RAG
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.
Two design patterns, Task Decomposition and Retrieval-Augmented Generation, are formalized for GenAI-based systems, impacting software quality attributes and AI development phases.
Foundation Models GenAI-based systems Task Decomposition Retrieval-Augmented Generation software quality attributes flexibility maintainability safety security dataset creation model training model evaluation deployment