FujiSim is a custom GPT system designed to recommend film simulation recipes for Fujifilm cameras based on contextual factors like lighting, subject type, and aesthetic direction. The model reasons strictly from a curated CSV dataset (my personal library of film recipes) and returns tailored suggestions grounded in structured metadata rather than generic online advice.
I’ve always loved Fujifilm cameras for their film simulations. They let me fine-tune the color science of each shot to match mood and personality. Over time, I built a collection of custom film simulation recipes that reflect my aesthetic style.
But choosing the right recipe for a given moment isn’t always straightforward. Light changes. Skin tones behave differently. Some simulations thrive in bright sun but fall apart indoors. And because film simulation preference is deeply personal, generic online recommendations often feel too broad or mismatched.
I wanted a solution that understood my personal preferences for film simulations, drawing knowledge from my personal library of film recipes and setting to pick the best recipe to experiment with.
Film simulation choice is influenced by subtle and highly contextual factors:
Existing tools don’t account for all of these at once. ChatGPT can guess, but its suggestions come from general knowledge and not my curated recipe set. And manually evaluating recipes every time is slow and inconsistent.
Design a system that:
I built FujiSim, a custom GPT that ingests my recipe catalog (CSV) and uses a scoring framework to recommend the best-fitting film simulation for any shooting scenario.
The core design principles were:
This makes FujiSim behave like a personalized color-science assistant.
1. Structured recipe catalog
Every recipe is annotated with multi-tag metadata, including:
These tags use consistent, machine-readable formatting (e.g., portrait_skin_medium).
2. Context understanding
User prompts like:
Indoor tungsten portrait, medium skin tone, warm cinematic vibe
are parsed into 4 dimensions:
3. Weighted scoring system
Each recipe is scored using:
FujiSim ranks the top matches and explains the reasoning behind each.
4. Strict knowledge boundaries
To prevent hallucination:
This creates predictable, controlled behavior — crucial for an AI tool working with personal taste.
Structured reasoning with AI: Explored how to translate subjective creative preferences into structured metadata that an AI can interpret consistently.
Designing controlled outputs: Preventing hallucination taught me how to set strict boundaries for LLMs, something highly relevant in AI system design.
Human–computer interaction for creative tools: FujiSim sits at an interesting intersection of aesthetic intuition and technical logic, aligns closely with my interest in emerging technologies.
Building personalized AI assistants: I learned how to create tools that adapt to personal workflows rather than broad generic use cases.
FujiSim started as a personal experiment to make my photography process smoother. It grew into a full demonstration of how AI can reason over structured datasets and support creative decision-making without losing personal nuance. The experience building and using FujiSim reminded me that I enjoy building tools that support my creative pursuits and not just what’s technically possible.
