Optimizing RAG Pipelines with Katib: Hyperparameter Tuning for Better Retrieval & Generation

Leveraging Katib for efficient RAG optimization. Feb 21, 2025 • Varsha Prasad Narsing (@varshaprasad96) • 7 min read katib As artificial intelligence and machine learning models become more sophisticated, optimising their performance remains a critical challenge. Kubeflow provides a robust component, Katib , designed for hyperparameter optimization and neural architecture search. As a part of the Kubeflow ecosystem, Katib enables scalable, automated tuning of underlying machine learning models, reducing the manual effort required for parameter selection while improving model performance across diverse ML workflows. With Retrieval-Augmented Generation ( RAG ) becoming an increasingly popular approach for improving search and retrieval quality, optimizing its parameters is essential to achieving high-quality results. RAG pipelines involve multiple hyperparameters that influence retrieval accuracy, hallucination reduction, and language generation quality. In this blog, we will explore how Katib can be leveraged to fine-tune a RAG pipeline, ensuring optimal performance by systematically adjusting key hyperparameters. Since compute resources are scarcer than a perfectly labeled dataset :), we’ll use a lightweight Kind cluster (Kubernetes in Docker) cluster to run this example locally. Rest assured, this setup can seamlessly scale to larger clusters by increasing the dataset size and the number of hyperparameters to tune. To get started, we’ll first install the Katib control plane in our cluster by following the steps outlined in the documentation. In this implementation, we use a retriever model , which encodes queries and documents into vector representations to find the most relevant matches, to fetch relevant documents based on a query and a generator model to produce coherent text responses. To run Katib, we will use the Katib SDK , which provides a programmatic interface for defining and running hyperparameter tuning experiments in Kubeflow.