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Context Goldilocks Principle
AI performance is optimized when you provide just the right amount of context - not too much (causing confusion/hallucination) and not too little (causing generic responses), but precisely what's needed for the specific task
Decision Rule
Before providing context to AI, ask: 'Is this specific piece of information directly relevant to producing the exact output I want?' Include only what passes this test
How It Works
Too much context creates 'context rot' where important information gets lost in noise and the AI becomes confused about priorities. Too little context results in generic, unhelpful outputs. The right amount creates focused, high-quality results
Failure Modes
Context dumping - providing everything you have available
Under-contextualization leading to generic outputs
Not updating context as needs evolve
Mixing instructions with reference material
Example Decision
“When creating a marketing analysis skill, include specific KPI definitions and example reports, but exclude general marketing theory or unrelated company information that doesn't directly inform the analysis task”