Our deep thoughts about complex AI issues, architectures, and implementation strategies, going beyond the surface.
RAG (Retrieval Augmented Generation) combines LLMs with external data sources for enhanced AI responses. While perfect for simple Q&A and chatbots with custom data, our real-world implementation revealed significant limitations with accuracy, debugging, and complex queries that required a more sophisticated multi-layered approach.
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Welcome! I'm Chris from Aloa, and this is the first post in a series where we're building an AI medical transcription app from scratch. If you're interested in healthcare AI or just want to see some practical applications of technology, this series is for you. Today we're focusing on the transcription engine, which is the core technology that converts a doctor's dictation into text.
We built a RAG-powered recommendation system that matches user preferences against thousands of blog posts. It works great now, but we learned some expensive lessons about rerankers, vector databases, and data structure along the way. Here's what we wish we'd known before we started.
We analyzed 2,540 top keywords to see just how bad it is—and who’s hit hardest.
RAG (Retrieval Augmented Generation) combines LLMs with external data sources for enhanced AI responses. While perfect for simple Q&A and chatbots with custom data, our real-world implementation revealed significant limitations with accuracy, debugging, and complex queries that required a more sophisticated multi-layered approach.
Welcome to a non-technical founder’s take on the AI timeline that might just become reality...
Learn how different AI models perform for content creation based on our real-world testing. Discover the unique strengths and weaknesses of Claude, GPT, DeepSeek, and Gemini for producing marketing content.