Welcome to Knowledge Crystal. I’m excited to share this space with you.

Why This Site Exists

Over the past few years of building AI systems in production, I’ve learned something important: reliability beats coolness. Teams don’t need the fanciest model or the most impressive benchmark—they need systems they can trust.

When you’re deploying an AI system to handle customer support, inform business decisions, or extract critical information, hallucinations aren’t just embarrassing. They’re expensive. They damage trust, create liability, and waste everyone’s time.

This site is my attempt to share what I’ve learned about building AI systems that actually work.

What You’ll Find Here

Projects

Real work from the trenches. I’ll be sharing the systems I’ve built, the challenges I’ve faced, and the solutions that actually worked. Each project represents hard-won lessons about what it takes to make AI reliable.

Articles

Thoughts on:

  • Building reliable LLM systems: Architecture patterns, evaluation strategies, and production gotchas
  • GraphRAG and knowledge graphs: How to ground AI responses in structured knowledge
  • Evaluation and testing: Making sure your AI system does what you think it does
  • Team practices: How to build AI systems collaboratively without losing your mind

A Conversation

I’m building this in public because I believe we need more honest conversations about AI engineering. Not “look at how smart our model is,” but “here’s how we actually built something reliable.”

What’s Coming

Soon you’ll find:

  • A deep dive into GraphRAG implementation
  • Evaluation frameworks that actually catch hallucinations
  • Case studies from real projects
  • Tools and code you can use in your own work

Let’s Build Something Reliable

If you’re working on AI systems and want to discuss reliability, evaluation, or making AI actually trustworthy, I’d love to hear from you.

Reach out on LinkedIn, GitHub, or email: denson@knowledgecrystal.com.

Here’s to building AI systems teams can believe in.

—Denson