Just now · 12 min read · Your RAG application answered a customer question with yesterday's pricing. A support agent trusted the response and quoted the wrong number. The customer escalated. Sound familiar? Retrieval-Augmented Generation (RAG) connects large language models ...
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5h ago · 1 min read · What I built toward The philippine-law-assistant/backend is a FastAPI service aimed at law students: chat, practice tests, and voice-oriented APIs, with Philippine Constitution and law content wired through services for retrieval and generation. Core...
Join discussion20h ago · 9 min read · Building Persistent AI Agent Memory Systems That Actually Work Photo by Andrey Matveev on Pexels Here's a startling fact: 87% of AI agents fail at multi-turn conversations because they can't remember context beyond their immediate training window. W...
Join discussion1d ago · 2 min read · Most RAG systems work like this: Take user query → convert to embedding → search → generate answer But here’s the issue: User queries are often too short, too vague, and missing context. And because o
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2d ago · 26 min read · If you’ve ever built a sensor pipeline before, you know how it usually ends. Data flows in, you set some thresholds, draw a pretty chart, and call it a day. And honestly? That’s fine. Until it isn’t.
Join discussion3d ago · 10 min read · 🧠 Introduction You know that feeling when you're chatting with an AI and it gives you an answer that's completely off-base because it doesn't know about your company's latest project? Yeah, I've been
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