Deliberate construction and management of the information fed into an LLM's context window, treated as a first-class engineering problem spanning retrieval strategy, knowledge structure, memory systems, and token efficiency.

Developer tooling spans shell ergonomics, CI infrastructure, type-safe validation, test analytics, and AI-assisted automation, with sources collectively showing that the best tools reduce friction and surface failures earlier without adding their own failure modes.

The practical discipline of building, evaluating, and operating systems that use large language models, spanning knowledge architecture, agent control flow, inference optimization, and the human and organizational costs of getting it wrong.

The ecosystem of tools for running, serving, and organizing knowledge for LLMs spans local inference runtimes, documentation platforms, and structured knowledge bases, with transparency and context efficiency as recurring concerns.

RAG grounds LLM outputs in external documents at query time, but its limitations around cross-document synthesis have pushed practitioners toward alternatives like compiled knowledge bases that pre-synthesize information into structured, queryable Markdown.