AI Engineering Blog

Learn how to build scalable RAG (Retrieval Augmented Generation) systems that can handle millions of queries. From vector databases to advanced chunking strategies, I'll show you the architecture patterns that work in production.

Stop guessing if your LLM is performing well. Learn the systematic evaluation framework I developed at Northeastern University to measure accuracy, safety, and business impact of AI models.

I've built agents with every major framework. Here's my honest comparison of LangGraph, CrewAI, AutoGen, and others—with real performance benchmarks and production lessons learned.

I optimized a vector database from 10-second queries to 10-millisecond responses. Here's exactly how I did it, with benchmarks, code examples, and the mistakes that cost me weeks.