The systems, abstractions, and operational layers that make AI models usable at scale, from compute and caching to routing, governance, agent hosting, and credential management.

Benchmarks measure model or system capability, but their results are only as meaningful as their design — a recurring problem across LLM, multi-agent, and vision tasks, where tests built for one context are routinely applied to contexts they cannot capture.

LLM engineering spans the full stack of building with large language models: training, inference optimization, agent architecture, harness design, and the operational tradeoffs that determine whether model capability translates into reliable software.

LLM inference covers how language models generate tokens from a prompt — spanning hardware constraints, serving architecture, caching strategies, quantization, routing, and cost — and has become its own engineering discipline as scale and cost pressures intensify.

LLM orchestration covers the control structures, harness designs, and coordination patterns that govern how language models are invoked, sequenced, and supervised — whether in single-agent loops or across distributed multi-agent pipelines.