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Too much context buries what matters; too little forces follow-up questions. The trick is matching your detail level to what your manager actually needs to decide and act.
- Remind your manager where you left off and be explicit about what you need from them—don't make them guess whether this is an FYI or a request for approval.
- Cut details that don't serve your main point (like exact dates when relative time matters), but add more context when decisions are irreversible, expensive, or customer-facing.
- Lead with your recommendation and reasoning, then put supporting details below so your manager can read as much or as little as needed.
Modern AI models are capable enough to make meaningful decisions about how to solve problems, so you should tell them your priorities and context instead of just giving them a narrow spec. This lets them suggest better approaches and avoid wrong assumptions about what you actually want.
- Early AI agents needed explicit step-by-step instructions; now they fail because they misunderstand your goals, not because they're confused about how to execute
- Sharing broad context—your long-term aims, constraints, and what tradeoffs matter—lets models suggest improvements you wouldn't have thought to specify
- Explicitly ranking your priorities (e.g., "I care less about performance than observability here") gives models the information they need to make smarter choices