Context engineering transforms AI coding tools from unpredictable autocomplete systems into reliable development partners through systematic information architecture rather than ad-hoc prompting. The approach involves dynamic curation of project knowledge, structured workflows, and tool-specific optimizations that deliver measurable results—including 26% average productivity improvements and 78% reduction in specification ambiguity. Success requires treating context as persistent architecture using proven frameworks like PRD-driven development, advanced Cursor configurations with .cursor/rules, and Claude's structured conversation patterns. Engineers who implement these systematic approaches report sustainable gains within weeks, with proper context management reducing AI implementation errors by 80-90% while cutting API costs significantly.