Editorial

Thinking &
Engineering.

Hands-on articles on RAG, agents, model evaluation, and architecture choices in applied AI.

Why production RAG is harder than it looks

Chunking, latency, cost, and evaluation: what changes when you leave the notebook and enter corporate SLAs and messy data.

Patterns to decompose tasks, limit scope, and observe multi-step flows without turning your product into prompt theater.

Beyond generic benchmarks: how to define success for your use case, measure regression, and involve the business.

Cost, data volume, latency, and maintenance criteria to decide between iterating prompts, RAG, or training/adapting a model.

Newsletter coming soon. Meanwhile, reach out about AI architecture, roles, or technical partnerships.