Tailoring foundation models for your business needs: A comprehensive guide to RAG, fine-tuning, and hybrid approaches

Foundation models (FMs) have revolutionised AI capabilities, but adopting them for specific business needs can be challenging. Organizations often struggle with balancing model performance, cost-efficiency, and the need for domain-specific knowledge. This blog post explores three powerful techniques for tailoring FMs to your unique requirements: Retrieval Augmented Generation (RAG), fine-tuning, and a hybrid approach combining both methods. We dive into the advantages, limitations, and ideal use cases for each strategy.

AWS provides a suite of services and features to simplify the implementation of these techniques. Amazon Bedrock is a fully managed service that offers a choice of high-performing FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Amazon Bedrock Knowledge Bases provides native support for RAG, streamlining the process of ...