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If we improve the tone prompts using each user’s LinkedIn writing history, then we will increase average tone match score from 5.26 to 8 out of 10.

Tone baseline analyses run on 10 users on March 9 showed scores ranging from 2.1 to 6.9 out of 10, averaging 5.26/10. AI-generated content is measurably diverging from how these users actually write.

The current prompts lack sufficient personalization signal. Each user’s existing LinkedIn post history contains enough stylistic data to close the gap.

Same 10-user cohort. The improved tone prompt — trained on each user’s LinkedIn post history — runs live for 14 days. All 10 users are re-scored using the same methodology as the March 9 baseline. A one-week check-in reviews directional movement before the final read.

Start Mar 16, 2026
Prompt deployed & live
Check-in Mar 23, 2026
Monday meeting — directional read
End Mar 30, 2026
Final re-score & decision

Tracked in tone_delta_experiments — same scoring model, same 10-user cohort as March 9.

Baseline (Mar 9) 5.26
avg / 10  ·  range: 2.1–6.9
Target (Mar 30) 8.0
avg / 10  ·  +52% lift
User Baseline Score Posts Analyzed LinkedIn Posts (Total)
Kamil Rextin 2.1/10 9 463
Daryl Driedger 3.9/10 11 69
Alejandra Céspedes 4.8/10 4 15 △
Matt Dorman 4.8/10 6 223
Eric Voyer 5.7/10 6 105
Olanike A. Mensah 5.9/10 10 0 △
Meeky Hwang 6.2/10 16 165
Rameez Faheem 6.5/10 6 12 △
Bill Wilson 6.8/10 5 710
Renée Cormier 6.9/10 10 422
Cohort Average 5.26/10

△ Flagged — thin or missing LinkedIn data. Scores for these users should be read with lower confidence.

Hit 8/10 Roll improved prompts out to all content module users.
Miss 8/10 Identify which users diverged furthest and determine whether the gap is a data problem (insufficient post history) or a model problem.