feat: add Context Engineering practitioner framework to LLM Engineering Best Practices#3
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What this adds
Adds Context Engineering: The Skill That Separates AI-Native Engineers to the LLM Engineering Best Practices section.
Why it belongs here
The article proposes a specific, practical framework for how software engineers should structure information when working with AI models: 4 context types (structural, intent, state, constraint) × 3 levels of scope (per-prompt, session, system).
It positions context architecture — not prompt phrasing — as the foundational engineering skill for AI-native developers working on real codebases, not demos.
Differentiation from existing entries in this section
The current LLM Engineering Best Practices entries cover broad product-level concerns (hard stuff, defensibility, interpretability) or foundational AI understanding. This article fills a practitioner gap: a reusable mental model for engineers who need to make AI tools work reliably in complex software environments.
The 'context engineering' angle is distinct from prompt engineering — it's about information architecture, not wording.