A lot of disappointing AI output gets blamed on the model. In practice, the input is often the real problem: one screenshot is missing, a PDF contradicts a note, or a copied summary has quietly lost its source.
A longer prompt does not fix incomplete evidence. I now prepare a small, reviewable context packet before asking any assistant to reason about a task.
1. Normalize the sources
Start with the actual material: screenshots, PDFs, files, copied text, notes and links. Extract the facts, but keep every fact connected to its source. Confidence should remain visible too; uncertain OCR is not the same thing as a verified sentence in a document.
2. Surface conflicts instead of smoothing them over
If two sources disagree, preserve the conflict. If an important answer is missing, list it as an open question. A polished summary that hides uncertainty is more dangerous than a rough brief that admits what it does not know.
3. Review privacy explicitly
Detection should be a warning, not an automatic deletion pass. Removing a name, date or identifier can change the meaning of the evidence. I prefer a keep, replace or remove decision for every detected detail, followed by one final review before anything is shared with an AI service. Privacy detection is useful, but it is not a promise of anonymity.
4. Export a reusable brief
Structured headings, source references and a version history make the same reviewed context usable with different assistants. That avoids rebuilding the prompt from scratch and keeps the evidence separate from whichever model happens to process it.
I turned this workflow into AI Brief for iPhone: on-device OCR, PDF reading, duplicate checks, fact extraction and privacy review, with explicit sharing only after review. It is free to start with one optional lifetime unlock and no subscription. The full context-preparation checklist is here: https://alice51849.github.io/ios-app-guide/guides/aibriefpack.html
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