Harnessing AI for Career Development: My Journey with Career Gemini - Google AI Hackathon

The Google AI Hackathon was an opportunity to spend some time playing around with Large Language Models (LLMs | Google Gemini) and develop a project that provides clear value to Gemini users. This event hosted by Devpost was a deep dive into the potential of 谷歌 's powerful APIs and tools. My journey through this hackathon was both challenging and enlightening, providing me with hands-on experience and a clearer view of AI’s potential in transforming everyday applications.

See the full project submission here: https://devpost.com/software/career-gemini

Inspiration

Almost everyone has looked for a job or wondered what their career would look like. It might have been a first job, a job after college, or a painful transition after being fired. And while most people have a good idea of possible jobs they might tolerate, it is much more rare to find people planning their career and managing the way to get there. That is because career planning spans years, not hours or days. And it requires lots of elements that are not interconnected. These include job boards, career coaches, online courses and certifications, internships and projects, networking, various assessments and interview practice. But how to organize them into something that resembles a plan or a strategy and how to keep track of these diverse activities so that you can make progress in your career? Importantly, how do you avoid scams, budget and develop patience for each step of the way as you make progress.

From a practical side, I was also interested to see how various Google tools such as Sheets, docs, even sites can be used to manage and improve career advancement.

What it does

Career Gemini is a tool that brings together various aspects of job search, coaching, planning and automated tasks to simplify career planning. By leveraging LLms capability, the application can integrate many of the functions that a career coach would serve and link goals, assessments, gaps and tasks into a dynamic plan that will help you get the career you deserve at the time when you are ready.

How we built it

Using Google Gemini, Langchain and Streamlit, we developed a chatbot-based application that anyone can use online. To enhance the LLM, we used data from coaching textbooks, posted jobs, courses and resumes. Leveraging these data points, we personalize the experience by asking the user about their background, relevant experience and plans to develop assessments and planning for their career.

We found it useful to prototype and experiment with Google Gemini in AI Studio as well as to leverage the API in Google Colab. Github’s Codespace provided us with the right development environment to develop and test early prototypes of the application.

Challenges we ran into

One of the challenges we ran into was prompt engineering. Not only is it important to collect user information and use it for the application, it is also extremely important to keep it truthful. While LLMs do a great job turning chat replies into something meaningful, we needed to avoid fabrications on resumes and untruthful information about outcomes of certain tasks.

We also ran into some issues with the interface (time constraints), scraping data that we need and bringing the pieces of the application into a single, logical workflow.

Accomplishments that we're proud of

We figured out how to improve prompts to achieve various tasks, such as assessment of the user skills, using langchain to deal with PDFs and Google sheets, produce visualization and leverage Streamlit for rapid prototyping. In our discussions around front-end user experience, we identified interesting UI components that will make this application user-friendly when it is fully developed.

What we learned

Each member of our team brought relevant skills and experiences from their current work, studies and struggles in their own career. Getting to work together on this project enabled us to deal with LLMs in a more practical way and usse it to think about various challenges LLM-based solutions will face when producing meaningful, practical and useful results.

What's next for Career Gemini

While researching existing solutions for job search and online learning, we found many promising applications that leverage LLMs for short-term tasks. The hope is that these elements can be integrated in an ecosystem of apps and services offered by real people to make career planning and professional development more predictable for talented people worldwide.

My Contribution: Bridging Ideas with Implementation

As a core part of our project, I took on the challenge of developing prototypes using Google Colab. This involved work with prompt engineering for the Google Gemini API, developing Python code using tools like LangChain and integrating interactive visualizations with Plotly to bring data-driven insights to life visually.

One of the most exciting parts for me was taking the raw idea to a working prototype and having an amazing team we could work together with and divide up the tasks.

Team Contributions: A Synergy of Skills

- David Garwin : As a Senior Software Engineer at Reddit, David was instrumental in developing the architecture design. His expertise was crucial in implementing effective workflows within Streamlit, ensuring our backend processes meshed well with the frontend interface.

- Kevin Ko : With a rich background as a Senior Data Scientist and previous stints at companies like Capgemini and Amazon, Kevin designed prototype pipelines for data analysis on Google Cloud. His contributions were vital in assisting with the nuanced aspects of prompt engineering, helping to refine the accuracy and effectiveness of our AI tools.

- Artiom F. : Artiom’s extensive experience as a Senior Solutions Engineer shone through his work on web scraping and integrating functionality into the Streamlit application. His previous experiences at Amazon and Google played a significant role in his adept handling of APIs and implementation challenges.

- Kelsey Buckley: An aspiring UI/UX designer, Kelsey tackled competitor analysis and designed wireframes that greatly influenced our frontend design. Her delivery of the front-end design mockup was not just about aesthetics but about creating a user-friendly interface that would make our application accessible and engaging.

Real-World Application and Future Directions

Our project aimed to redefine how individuals approach career development, leveraging AI to automate and personalize the job search and career planning process. By integrating advanced AI techniques with user-friendly interfaces, we have created a tool that not only simplifies the job search but also empowers users to strategize their career growth effectively.

Looking ahead, the foundations laid during this hackathon promise exciting opportunities. The insights gained and the technologies harnessed point towards broader applications in various industries, where AI can play a transformative role in enhancing user experiences and operational efficiencies.

In conclusion, the Google AI Hackathon was more than just a competition; it was a learning ground and a launchpad for future innovation. My personal growth in understanding and implementing AI through practical application was matched by the collective progress made by our team. As we continue to explore and expand on our project, the potential to make a significant impact remains immense, driven by our continued passion and the robust capabilities of AI.

See the full project submission here: https://devpost.com/software/career-gemini

Learn more about me and my background: https://www.ebrodsky.site