RAISE: RAG Design as an Architecture Search Problem | AI Research

RAISE: RAG Design as an Architecture Search Problem
Retrieval-augmented generation (RAG) systems rely on a complex web of design choices, including how to rewrite queries, chunk documents, retrieve information, and rerank results. Currently, these settings are often chosen through trial-and-error or simple heuristics, which makes it difficult to compare different systems or reproduce results. This paper introduces the RAG Intelligence Search Engine (RAISE), a framework that treats RAG design as an "architecture search" problem. By standardizing the search space and evaluation protocols, RAISE allows researchers to systematically optimize RAG pipelines under controlled conditions.

A Unified Framework for RAG Optimization

RAISE functions as a benchmark environment that connects a parameterized RAG pipeline with various optimization algorithms. The framework is built on three core components: a pipeline abstraction that defines the possible configurations, an evaluation layer that scores performance on specific tasks, and a controller interface that allows different optimization algorithms to propose and test configurations. This modular design ensures that researchers can swap out optimization strategies—such as random search, Bayesian optimization, or reinforcement learning—while keeping the underlying RAG pipeline and evaluation metrics consistent.

Testing Diverse Search Strategies

To understand how different optimization methods perform, the authors tested 13 distinct algorithms across seven diverse datasets, including text-based and multimodal tasks. These datasets were chosen to stress-test different parts of a RAG system, such as long-document retrieval, multi-hop reasoning, and visual grounding. By using a fixed computational budget for each algorithm, the researchers were able to create a fair, head-to-head comparison of how different search biases (like local trajectory search or evolutionary strategies) navigate the complex landscape of RAG configurations.

Performance is Task-Dependent

The study reveals a critical finding: there is no "universally superior" optimization strategy for RAG systems. Instead, performance is highly dependent on the specific task. For example, a method that excels at multi-hop reasoning might underperform in a long-context or multimodal environment. Because the best-performing optimizer changes depending on the dataset, the authors caution against relying on aggregate rankings. Instead, they suggest that researchers should view RAG architecture search as a series of optimizer–environment interactions, where the choice of search method should be tailored to the specific structure and requirements of the task at hand.

Insights into Pipeline Design

Beyond comparing optimizers, RAISE provides a common experimental basis for identifying which pipeline modules have the greatest impact on performance. The results suggest that different tasks require different configurations: long-document retrieval tasks benefit significantly from query rewriting and pruning, while multi-hop reasoning tasks are more sensitive to retrieval depth. By providing this standardized substrate, the authors hope to move the field away from ad-hoc tuning and toward a more systematic, reproducible approach to building high-performance RAG systems.