Project case study
Bad to Improved Prompt Model
Fine-tuned Llama workflow for upgrading weak image prompts
Fine-tuned Llama model that rewrites weak image generation prompts into detailed, high-quality ones.
Problem
Image quality often depends more on prompt quality than people expect, yet many users do not know how to expand a vague idea into a precise prompt that a model can use well.
Solution
I fine-tuned a Llama-based system to transform rough prompts into stronger, more descriptive prompt candidates for image generation workflows.
Impact
This reduces prompt-engineering friction and demonstrates a focused LLM use case with clear before-and-after value.
Stack and implementation notes
This project combines product thinking with technical implementation. The goal was not only to prove the underlying model or workflow, but to shape it into something understandable and usable for real people.
Technologies used here include Python, Llama, Supervised fine-tuning, Prompt engineering. The stack was chosen to keep the delivery practical while still leaving room for experimentation, iteration, and deployment.