aiqbench analysis
This presentation details architectural strategies for building production-grade AI applications by leveraging Redis to manage memory layers, mitigate context rot, and control API costs under strict latency constraints. The core focus shifts from simple prompting to engineering robust systems capable of handling long-term memory and natural conversations in real-world scenarios.
Presentation: Beyond Prompting: Context Engineering for Production-Grade AI

Ricardo Ferreira discusses moving beyond simple prompt engineering to build production-grade AI applications. He shares practical architectural strategies for integrating long-term and short-term memory using Redis, managing LLM token limits via summarization, mitigating context rot with reranking and semantic caching, and controlling exponential API costs under strict latency constraints. By Ricardo Ferreira
Ricardo Ferreira: My name is Ricardo Ferreira. We're going to be talking about this context engineering stuff. This presentation is actually going to be a mix of lessons learned when I've developed a specific solution that I'm going to talk about. I learned the easy and the hard way. Also, it's going to be a mix of storytelling, because these applications that I've developed actually have a story. I was setting up my Alexa device, which has to do with what I built. I work for Redis. Let me tell the story about what I actually have built, so you can understand where I'm coming from. I work inside Redis from this part of this team called developer experience. One of the things that we started doing, probably I think about two years ago, is to build an open-source project called Agent Memory Server, or AMS, on top of Redis. The idea of this open-source project was to create a very quick, fast memory layer on top of Redis, so developers can build either short-term memory and long-term memory on top of it. Build more natural human-like conversations with it. Then our team that developed this project, we're also being…
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