Résumé
L'auteur a créé un outil pour construire des profils de parties prenantes basés sur des preuves en utilisant Claude Code. Chaque profil contient des traits étiquetés comme des faits, des inférences ou des hypothèses, avec des citations exactes. Un script vérifie l'exactitude des citations et un système de contrôle évalue les prédictions.
Pourquoi c’est intéressant
Ce projet est intéressant car il utilise Claude Code pour créer des profils de parties prenantes personnalisés et basés sur des preuves, ce qui peut améliorer la précision des prédictions et la qualité des interactions avec les parties prenantes.
Comment Claude est utilisé
L'auteur a utilisé Claude Code pour développer des profils de parties prenantes basés sur des preuves, en intégrant des citations exactes et en distinguant les faits, les inférences et les hypothèses. Le code et les tests ont été générés par Claude Code.
Idées dérivées
- 01
Personnalisation de propositions commerciales
Créer un outil d'aide à la rédaction de propositions commerciales en utilisant des profils de parties prenantes pour personnaliser les contenus.
- 02
Suivi des interactions avec les parties prenantes
Développer un système de suivi des interactions avec les parties prenantes pour améliorer la précision des profils et des prédictions.
- 03
Évaluation de la qualité des profils
Concevoir un outil d'évaluation de la qualité des profils de parties prenantes pour détecter les biais et améliorer la fiabilité.
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I work in presales, and before I send a proposal I want to know what each person who'll read it is going to object to. The usual trick is a persona prompt ("you're a skeptical CFO, review this"). What comes back is the objections any careful reader would raise, with nothing that tells you which ones belong to the CFO you're actually dealing with.
So for a couple of months I've been using Claude Code to build profiles of the real people, from my own notes. Each trait is labeled as a fact, an inference or an assumption, and every fact carries the exact quote it came from. Where the profile has no evidence, the reviewer has to say "no basis" and stop there. Each person gets their own reviewer subagent, and next to it runs a control reviewer that knows only the job title: whatever the control also says is the generic stuff a persona prompt would have given me anyway.
Two things went wrong along the way.
The quotes nobody checked. A script verifies that every quote in a profile really exists in its source file, because a profile built on made-up quotes is just a persona prompt with footnotes. For two weeks it said "all good" on 5 of the 14 profiles. They weren't clean: the quotes were in a format the parser didn't recognize, and it treated "found nothing to check" as "found nothing wrong". Now anything it can't parse blocks, and the health check refuses to print "healthy" over a check that couldn't run.
In those same two weeks I also built 14 profiles and compared zero predictions with what people actually said in the meeting, so I had no evidence that my profiles beat a plain persona prompt, which is the whole point. Building a profile is fun and grading it afterwards is a chore, so the grading never happened. Now the review freezes its predictions before the meeting and the next session nags me about the overdue ones. I'm not convinced that's enough.
Two questions for anyone who has built personas for reviews or simulations:
• how do you tell whether your personas actually beat a plain role prompt?
• how do you catch checks that pass because they never really looked?
Claude Code wrote all the code and the tests, the design and the rules are mine. It's MIT, if you want to see how it's wired: https://github.com/FrancescoPolitano/beforehand