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.

Developer tooling spans the full surface area of software construction — version control, testing, shell ergonomics, AI coding assistants, and platform infrastructure — with a consistent theme: reducing friction without sacrificing correctness or security.

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.

The infrastructure, utilities, and integration layers built around large language models, spanning local inference runtimes, context management, MCP servers, knowledge organization, and provider-agnostic design patterns.

RAG grounds LLM outputs in external knowledge at inference time; recent work questions when vector similarity retrieval is the right tool and what alternatives — hierarchical indexing, KV caching, compiled wikis — better serve different workloads.