Deep Dives
Our deep thoughts about complex AI issues, architectures, and implementation strategies, going beyond the surface.
The Reality of RAG
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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ChatGPT Wrapper vs AI Product: What Are the Key Differences?
Learn the ChatGPT wrapper vs AI product difference so you can spot shallow tools, avoid AI washing, and pick tools that truly help your team.
Rules-Based vs AI-Driven Systems: How to Choose for Your First Product Version
Compare rules-based automation vs AI for your first product version. Learn when to start with rules, when to add AI, and how to avoid costly mistakes.
How to Make Any AI Model Safe through HIPAA Compliance
Is HIPAA compliance stopping you from using Claude, GPT-4, or Gemini in your healthcare projects? In this video, I reveal the "secret" that 95% of healthcare companies don't know: how to make almost ANY AI model HIPAA compliant through cloud provider hosting.
How to Learn AI: A Complete Beginner-to-Executive Learning Path
Discover how to learn AI with our complete beginner-to-executive learning path. Explore effective AI training and learning resources on our blog!
Building an AI Medical Transcription App: Part 1 - The Transcription Engine
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.
Building an AI Recommendation System (RAG): 3 Lessons We Learned the Hard Way
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.
