Having looked at MCP, Models, and RAG, I realised that I’ve been mentally skirting around something that I don’t really understand, so I’m going to expose myself to some ridicule here and try to understand better: what’s the difference between AI and ML? Aren’t they just the same?

A short series of notes for myself as I learn more about the AI ecosystem as of September 2025. The driver for all this is understanding more about Apache Flink’s Flink Agents project, and Confluent’s Streaming Agents.

Having poked around MCP and Models, next up is RAG.

RAG has been one of the buzzwords of the last couple of years, with any vendor worth its salt finding a way to crowbar it into their product. I’d been sufficiently put off it by the hype to steer away from actually understanding what it is. In this blog post, let’s fix that—because if I’ve understood it correctly, it’s a pattern that’s not scary at all.

A short series of notes for myself as I learn more about the AI ecosystem as of September 2025. The driver for all this is understanding more about Apache Flink’s Flink Agents project, and Confluent’s Streaming Agents.

Having poked around MCP and got a broad idea of what it is, I want to next look at Models. What used to be as simple as "I used AI" actually boils down into several discrete areas, particularly when one starts looking at using LLMs beyond writing a rap about Apache Kafka in the style of Monty Python and using it to build agents (like the Flink Agents that prompted this exploration in the first place).

A short series of notes for myself as I learn more about the AI ecosystem as of September 2025. The driver for all this is understanding more about Apache Flink’s Flink Agents project, and Confluent’s Streaming Agents.

The first thing I want to understand better is MCP.