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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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.
AI Overviews Are Eating Your Clicks - Here’s What We Found After Analyzing 2,540 Top-Ranking Keywords
We analyzed 2,540 top keywords to see just how bad it is—and who’s hit hardest.
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.
AI 2027: A Non-Technical Founder’s Review
Welcome to a non-technical founder’s take on the AI timeline that might just become reality...
AI Model Selection: The Overlooked Secret to Better AI Content Creation
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.