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.
Read
The Problem: Defaulting to Cheaper Models During Dev
The wrong AI model can waste hours in development. Here’s why model choice matters more than it seems.
AI Projects Aren't "Set It and Forget It"
AI apps are not “set it and forget it.” Learn why model upgrades, provider changes, and ongoing testing are critical in production.
Prompting Lessons from Building an 8-Phase AI Vocabulary Tutor
What does it take to build an AI tutor? Here are 12 prompting lessons from building an 8-phase vocabulary coach.
How to Scope a SaaS MVP So Users Actually Use It
Learn how to scope a SaaS MVP that users actually use by focusing on adoption, feature priorities, and smarter early scoping.
How to Ask the Right Questions When Building AI-First Products
Learn the right questions for building AI-first products so you can choose smarter use cases, avoid risk, and launch with confidence.
Proof of Concept vs Production: Which One Should You Pay For?
Learn how to decide between proof of concept vs production and avoid common mistakes that slow down AI projects.
