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RAG (Retrieval-Augmented Generation) is not a new concept in AI, and unsurprisingly, when talking to companies, everyone seems to have their own interpretation of how to implement it. So, let’s start with a refresher. RAG (short for Retrieval-Augmented Generation) is a technique that injects relevant data from an external knowledge source directly into a prompt before sending it to a Large Language Model (LLM). “But wait, my model is already fine-tuned on my domain-specific data.

In February 2024, Kong became the first API platform to launch a dedicated AI gateway, designed to bring production-grade performance, observability, and policy enforcement to GenAI workloads. At its core, Kong’s AI Gateway provides a universal API to enable platform teams to centrally secure and govern traffic to LLMs, AI agents, and MCP servers. Additionally, as AI adoption in your organization begins to skyrocket, so do AI usage costs.

APIs are the digital lifelines powering modern applications, microservices, IoT devices, and everything in between. They act as the universal translators of data, ferrying information between diverse software platforms. API security encompasses the technologies, practices, and protocols dedicated to protecting these invisible workhorses from unauthorized access, data breaches, and malicious misuse.

The word on the street is that Ambient Mesh is the obvious evolution of service mesh technology — leaner, simpler, and less resource-intensive. But while Ambient Mesh is an exciting development, the reality is more nuanced. It is more than likely that a sidecar-based mesh is still a better fit for your workload and organization.

If you're a builder, you likely keep sending your LLM credentials on every request from your agents and applications. But if you operate in an enterprise environment, you'll want to store your credentials in a secure third-party like HashiCorp Vault or IDP provider and have the infrastructure inject the credentials for you dynamically.

Kafka delivers massive value for real-time businesses — but that value comes at a cost. As usage grows, so does complexity: more clusters, more topics, more partitions, more ACLs, more custom tooling. But it doesn’t have to be that way. If your team is managing Kafka at scale, here are five concrete steps you can take to immediately reduce cost and operational complexity — without sacrificing performance.