The Red Hat Blog: Expert insights for navigating tech complexity
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AI quickstarts: An easy and practical way to get started with Red Hat AI
Discover AI quickstarts, a new catalog of ready-to-run, industry-specific use cases that put the power of open source AI directly into your hands. Learn how to deploy, explore, and extend these practical examples to master Red Hat AI and take your AI ideas from experimentation to production.
Latest posts
Planning the design of your production-grade RAG system
Explore the challenges of RAG in production systems, including the myth of 'simple' RAG, the retrieval gap, advanced RAG techniques, and the operational reality of RAG. Learn how to manage latency, cost, data freshness, and access control in RAG systems.
Friday Five — March 6, 2026
The Friday Five is a weekly Red Hat blog post with 5 of the week's top news items and ideas from or about Red Hat and the technology industry.
MCP security: Implementing robust authentication and authorization
Learn how to implement robust authentication and authorization mechanisms for MCP servers, clients, and other components in agentic systems. Discover best practices for open MCP ecosystems, enterprise Kubernetes/Red Hat OpenShift environments, and internal/service-to-service deployments.
How does real-world AI deliver value? The Ask Red Hat example
Today, Ask Red Hat has evolved from a proof of concept into a sophisticated production reality. As of late 2025, it has served over 50,000 unique users and handled more than 450,000 messages.
Scaling the future of Open RAN: Red Hat joins the OCUDU Ecosystem Foundation
Red Hat has officially joined the Linux Foundation’s OCUDU Ecosystem Foundation to accelerate the development of open, carrier-grade software stacks for 5G and 6G networks using Red Hat OpenShift.
Featured stories
Platform engineering for AI agents ft. Tushar Katarki
What Is Product Security?
Open data and the AI resilience premium
Red Hat AI Inference Server
Red Hat AI Inference Server optimizes model inference across the hybrid cloud, creating faster and more cost-effective model deployments
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