Hey devs! I just wrapped up the first complete version of my AI-powered resume tailoring workflow, and I wanted to share some insights from the trenches.
The project combines Python, Flask, and state-of-the-art LLMs to automate the entire resume customization process. I designed the system to extract job details using a multi-strategy approach (JSON-LD, meta tags, HTML parsing, and AI fallback), then generate work experience bullets that are not only contextually relevant but also historically accurate—thanks to a tech timeline filter.
One of the biggest challenges was ensuring output quality. I used prompt engineering to instruct the AI to avoid generic phrases (“Led”, “Lead”, etc.), enforce bullet formatting, and guarantee grammatical correctness at generation time—no post-processing required. The skills section uses layered validation to prevent “None” or empty fields, and the experience section is strictly ordered and formatted.
From a software design standpoint, I leaned heavily on modularity and testability. Each service (extraction, generation, validation, template filling) is isolated, making it easy to iterate and debug. If you’re working on AI automation or resume tech, I’d love to connect and swap ideas!
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