Browsing: AI Applications

If you’re building an LLM-powered product, you already know the tension: ship fast or die, but also “hey, can you…

Data poisoning in AI pipelines is when an attacker or a broken process intentionally or accidentally injects harmful patterns into…

If you’ve shipped an LLM-backed system into production, system prompt leakage is not a hypothetical risk. It’s something that shows…

Traditional observability assumes two things that stop being true the moment you ship LLMs to production: Inputs and outputs are…

Every AI product team eventually hits the same tension: be safe, be helpful, and be consistent at the same time.…

If your product uses an LLM, your prompt is not “some text.” It’s behavior config. Change it, and you changed…

They worry about the model “hallucinating.” They worry about prompt injection. They worry about the agent sounding confident while being…

If you’re shipping an LLM feature into production, you’re making a bet every time you change anything: prompt, model, retrieval…

If you’ve been reading advice about RAG vs fine-tuning, you’ve probably seen two equally confident camps: “Just use RAG. Fine-tuning…

Shipping an LLM feature feels deceptively easy. You get a demo working in an afternoon, everyone’s impressed, and then someone…