Task Decomposition and Retrieval-Augmented Generation (RAG) are formalized as design patterns for GenAI systems, showcasing their implementation in workflow generation for enterprise applications.
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https://arxiv.org/abs/2412.00239
Original Problem 🤔:
→ GenAI systems using Foundation Models are becoming increasingly complex and difficult to design at scale.
→ Current systems lack established design patterns for integrating AI components safely and efficiently.
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Solution in this Paper 🛠️:
→ Task Decomposition splits complex AI tasks into smaller, manageable sub-tasks for better control and efficiency.
→ RAG augments AI models with real-time environmental data to reduce hallucination and improve accuracy.
→ The paper demonstrates these patterns through a workflow generation system that converts natural language requirements into structured YAML workflows.
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Key Insights 💡:
→ Breaking down complex tasks improves scalability and maintainability of AI systems
→ RAG enhances security by controlling how AI interacts with environment data
→ Separating retrieval from generation allows better testing and error analysis
→ Design patterns significantly impact the entire AI development lifecycle
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Results 📊:
→ Successfully deployed in enterprise production environment
→ Improved workflow generation accuracy through structured decomposition
→ Enhanced system modularity and maintainability through pattern implementation
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