Context Engineering
Context Engineering strategically engineers context windows by selecting, prioritizing, and structuring information to maximize AI agent performance while minimizing token costs. Shorter, well-scoped inputs often yield sharper responses than massive contexts. Well-crafted context enables use of cheaper models without quality loss. Techniques include information prioritization, structured formatting, relevance filtering, and hierarchical organization. Key insight: clarity and relevance matter more than context size.
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