Breaking down Anthropic’s Claude Certified Architect track — the domains it tests, the production patterns it expects you to know, and a practical study plan for engineers who already ship LLM systems.
How to design Retrieval-Augmented Generation pipelines that hold up in production — covering indexing, hybrid retrieval, reranking, evaluation harnesses, and the operational realities of freshness, latency, and cost.
Liter-LLM re-imagines multi-provider LLM orchestration by putting a Rust runtime behind a clean Python API. This deep dive walks through its architecture, the PyO3 polyglot bindings, and the patterns for building production-grade AI pipelines.
From ‘Attention Is All You Need’ to modern mixture-of-experts and state-space hybrids: how the transformer family has evolved and what working engineers should know about the variants in production today.
Calling an LLM API is the easy part. Shipping reliable AI features in production demands evaluation pipelines, retrieval layers, observability, cost controls, and guardrails. Here is what real AI engineering looks like in 2026.