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Building RAG with enterprise open source AI infrastructure
- Retrieval-augmented Generation (RAG) is an AI technique that addresses critical gaps in traditional LLMs by incorporating current and new information that serves as a reliable source of truth for these models.
- Building a generative AI project, such as a RAG system, requires multiple components and services with crucial considerations for ensuring scalable, efficient, secure and cost-effective infrastructure.
- Hardware, cloud environment, operating systems, and generative AI software are some of the crucial RAG infrastructure considerations.
- Linux is the most widely used OS for AI applications due to its flexibility, stability, and extensive support for machine learning frameworks such as TensorFlow, PyTorch, and Hugging Face.
- RAG can be used in various applications, such as AI chatbots, semantic search, data summarization, and even code generation.
- The good news is that you can relatively easily blend different elements of the cloud together into hybrid cloud environments that offer the pros of each one while covering the flaws that single-environment cloud setups may present.
- Canonical provides workshops and enterprise open source tools and services that can advise on securing the safety of your code, data, and models in production.
- Canonical offers enterprise-ready AI infrastructure along with open source data and AI tools to help you kickstart your RAG projects.
- Enhance the security of your GenAI projects while mastering best practices for managing your software stack with Confidential AI.
- In conclusion, by leveraging open source tools and frameworks, organizations can accelerate development, avoid vendor lock-in, reduce cost and meet the enterprise needs.
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