Thinking out loud.
Writing about the hard parts of building AI systems in production. Not tutorials — lessons learned.
Designing Reliable LLM Systems
What it actually takes to build LLM-powered features that work 99.5% of the time. Covering structured outputs, fallback chains, and the retry patterns nobody talks about.
When Not to Use Agents
Agents are powerful but overused. Here's a framework for deciding when a simple chain, a DAG, or a hard-coded pipeline is the better choice over autonomous agents.
RAG Failure Modes in Production
After running RAG systems serving 500+ daily users, here are the failure modes that don't show up in tutorials: chunking disasters, embedding drift, and the retrieval-generation gap.
Cost vs Accuracy: The Tradeoffs Nobody Documents
Every ML system has a cost-accuracy frontier. I walk through real examples of navigating this tradeoff in production, from model selection to caching strategies.