I've been working as a GenAI developer for about a year at a service company after graduating, but I've started feeling a bit stuck regarding my career path. I've worked with technologies like FastAPI, LangChain, and have built simple RAG systems, but mostly, I've been tasked with small PoC projects. I lack strong mentorship, so I've only grasped the concepts at a surface level and feel I'm not advancing as much as I'd like. Recently, I ventured into frontend development, hoping to become a full-stack GenAI developer, but I'm even more confused about where to focus my efforts. If I can dedicate about an hour a day to improving my skills, should I dive deeper into ML fundamentals, concentrate more on backend systems, or continue learning GenAI frameworks? Any advice would be greatly appreciated!
3 Answers
It sounds like you're in a common situation! I would suggest you consider building a complete project from the ground up instead of just learning new frameworks. Maybe deploy your existing RAG system to an actual cloud infrastructure. That way, you'll get to understand real-world challenges and how to solve them, which is incredibly valuable.
One key piece of advice would be to stop hopping between frameworks and commit to building one solid end-to-end project. Take what you’ve learned from your RAG setup and turn it into a fully deployed app that includes authentication, database management, logging, and scaling. This hands-on experience will teach you way more than learning another tool without that practical application.
I can totally relate to the PoC struggle! I recommend focusing on backend development – dive deeper into concepts like databases, scaling, and process management. It sounds like you're already familiar with some GenAI frameworks, so a solid backend foundation will just elevate your skills further.
Thanks for the suggestion! Do you have any ideas on what kind of project I should build and what level of complexity you think is suitable?