Each example below is a piece of AI-assisted writing rendered in Trace format. Click into one to read the final text, then explore the prompts behind it. Some examples offer both session view (messages in chronological order) and unit view (paragraph-level annotations).
A startup CEO writes a public response to a security incident. The prompts reveal someone fighting the AI's instinct to produce corporate non-apologies — pushing past "we take security seriously" toward something a reader might actually believe.
An engineer writes a farewell email on her last day. The prompts show someone rejecting the AI's generic "incredible journey" language, insisting on specific memories and refusing to perform gratitude she doesn't feel.
A nonprofit founder writes a grant narrative. The prompts show someone stripping away every piece of grant-speak the AI reaches for — "empower underserved youth," "evidence-based methodology," "sustainable funding model" — until what's left is just the truth about 42 kids and their homework.
A startup founder writes a quarterly update to investors. The session record shows someone fighting the AI's instinct to spin bad numbers — pushing past "strong momentum" toward honesty about a missed target, then pushing back again when the AI overcorrects into doom and gloom.
A manager writes a recommendation for a departing engineer. The session record shows someone rejecting every generic recommendation phrase the AI reaches for — "talented engineer with a passion for excellence" — until what's left is specific enough to actually be useful.
The author uses AI to write a LinkedIn post about Trace itself. The session shows eleven rounds of steering — rejecting pitchy language, insisting it sound like curiosity rather than a product launch, fighting AI slop, and repeatedly trimming until the post says only what it needs to.