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.

Context engineering is the practice of deliberately constructing what an LLM receives in its context window — structuring, compressing, persisting, and retrieving information so agents produce reliable output across tasks and sessions.

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.

Software architecture shapes how systems behave under pressure, how teams reason about codebases, and how much complexity accumulates over time — spanning module design, state management, deployment topology, and the feedback loops that keep all three honest.