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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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.
How to Layer AI Into An Existing Product Without Breaking Them
Layer AI into an existing product with a safe, step-by-step approach that boosts value without risking stability or slowing your team down.
What It Actually Takes to Ship AI in Healthcare
AI note generation looks impressive, but real deployment is a systems challenge. See what it really takes to make clinicians actually use it.
Why Anthropic’s Agent SDK Is the Real Deal
Anthropic’s Agent SDK lets anyone build Claude Code-level agents in under an hour. Here’s why it’s redefining what AI agents can do.
We Built the Same AI Agent Twice: Here's Why the Slack Version Won
We built the same AI agent twice and got opposite results. Here's why where your AI lives, not what it does, determines adoption.