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Generative AI Workflow Design: Task Decomposition and RAG Offer a Path to Efficient, Robust Systems

New design patterns offer a solution to the growing complexity of building AI systems using foundation models.

Generative AI (GenAI) systems, powered by foundation models like large language models (LLMs), are rapidly moving from research labs to production environments. However, building robust and efficient GenAI applications poses unique challenges due to the models’ black-box nature, sensitivity to data shifts, and the sheer scale of their outputs. A new paper from researchers at ServiceNow addresses these issues by formalizing two well-known techniques—Task Decomposition and Retrieval-Augmented Generation (RAG)—as design patterns for building GenAI systems.

Breaking Down Complexity: Task Decomposition

Task Decomposition is a classic software engineering principle applied here to GenAI. Instead of asking a single model to perform a complex task, it’s broken down into smaller, more manageable sub-tasks. Each sub-task is then handled by a separate model or, potentially, the same model with different prompts.

Consider building a system that automatically generates detailed workflows from natural language descriptions. Instead of having a single model generate the entire workflow at once (a difficult task prone to errors), Task Decomposition divides this into sub-tasks:

  1. CreateFlow: Generate the basic workflow outline—a list of steps and their order.
  2. PopulateInputs: Populate the inputs for each step based on the preceding steps and available environmental data.

This division makes each sub-task simpler to implement, test, and debug, leading to a more robust and maintainable system.

Augmenting with Real-World Knowledge: RAG

Retrieval-Augmented Generation (RAG) enriches the GenAI model’s capabilities by giving it access to external knowledge bases. Instead of relying solely on the model’s internal knowledge (which can be limited and prone to “hallucinations”—fabricating information), RAG retrieves relevant information from databases, documents, or other sources. This information is then incorporated into the model’s input, improving the accuracy and reliability of the generated output.

In the workflow generation example, RAG is crucial for the PopulateInputs sub-task. The model needs access to the database schema, column names, and other environmental details to accurately populate step inputs. By using RAG, the model avoids having to memorize all this data in its parameters, making it more efficient and easier to maintain.

A Real-World Case Study: Workflow Generation

The paper presents a compelling case study of how these design patterns were applied to build a real-world GenAI application for workflow generation. The researchers detail their experiences, highlighting the benefits and trade-offs of each approach.

For instance, they found that Task Decomposition significantly reduced the labeling effort required for model training, allowing them to release a functional application more quickly. RAG was essential for achieving acceptable accuracy in the workflow generation process, preventing the model from fabricating information due to limited internal knowledge.

Impact on the Software Development Lifecycle

The researchers show how Task Decomposition and RAG impact the entire software development lifecycle, from dataset creation and model training to evaluation and deployment. They demonstrate how these design patterns can lead to improved software quality attributes such as scalability, modularity, maintainability, safety, and security.

Conclusion

The paper provides valuable insights for developers and researchers building GenAI systems. By formalizing Task Decomposition and RAG as design patterns, it offers a practical approach to tackling the unique challenges inherent in building robust and efficient GenAI applications, ultimately contributing to the advancement of the field. The case study emphasizes the importance of considering software engineering best practices throughout the entire development lifecycle of GenAI applications, avoiding the common pitfalls of building AI models in isolation from the broader system context.