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I turned vague fragrance preferences into a usable scent profile by combining perfume notes, dislikes, changing sensory perception, and real-world feedback. I started by telling AI which perfumes I already liked. Rather than immediately recommending more fragrances, it broke those perfumes down into their notes and broader families, such as floral, woody, musky, citrus, sweet, powdery, spicy, green, and amber. I used product information from a qualitative webshop I liked to support that analysis. I then added scents and individual notes I do not enjoy. That distinction matters because liking a perfume does not necessarily mean liking every ingredient in it. A note may work beautifully in one composition and become overwhelming in another. My perception of fragrance had also changed. After a long period of not smoking, many scents seemed to arrive more sharply than before. Perfumes I might once have experienced as soft or pleasant could now feel much more intense. The important part is that AI does not treat perfume notes as a fixed formula. Instead of assuming, “You like vanilla, therefore recommend vanilla perfumes,” it can learn something more nuanced: “You like vanilla when it is softened by certain notes, but dislike it when it is combined with others, and you currently experience certain sharp notes more strongly.” Price can become another layer rather than a separate search. Once a promising scent profile emerges, AI can look for fragrances with similar structures at different price points. The result is a personal scent map: not only a list of perfumes I like, but an evolving model of why certain combinations work for me. The best validation is eventually reaching the highly scientific fragrance classification: > “HALLELUJAH, HOW GOOD DOES THIS SMELL?!” 😂🌺 Step-by-step: 1. I listed perfumes I already enjoy. 2. I analyzed their notes, scent families, and recurring combinations, using product information from a qualitative webshop I liked. 3. I added notes and fragrance types I dislike on their own. 4. I described how perfumes actually feel when worn, using reactions such as sharp, soft, warm, clean, heavy, sweet, fresh, comforting, or overwhelming. 5. I used those reactions to refine my scent profile. 6. I generated new fragrance suggestions based on the emerging pattern. 7. When a perfume looked promising but was expensive, I asked for similar lower-cost alternatives. 8. I smelled or wore the suggested fragrances in real life. 9. I fed my reactions back into the model, including whether a scent was too sharp or sweet, became nicer after an hour, had an opening I loved but a dry-down I disliked, or was simply amazing. 10. I repeated the process until the recommendations became increasingly precise.