The tooling and architectural choices underlying AI agent deployments, covering orchestration strategy, memory systems, observability, and the tradeoffs between single- and multi-agent approaches.

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.

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.

LLM inference spans the full stack from VRAM constraints and quantization choices on consumer hardware to latency optimization in production agent services, with tooling debates about transparency, local runtimes, and cost-efficient alternatives to large models.

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.