HackStack

Project Info

case-GPT

Devpost

Inspiration

The inspiration for Case-GPT arose from the desire to create a realistic and interactive platform for practicing consulting casing interviews. Combining a passion for artificial intelligence and professional development, the goal was to provide individuals with a low-pressure environment to hone their problem-solving skills and receive real-time feedback.

What it does

Case-GPT acts as an AI-powered interviewer, engaging in interactive casing interviews with human participants. The AI presents business cases, asks probing questions, and evaluates responses. Participants analyze problems, develop solutions, and communicate recommendations. The AI dynamically adapts its questioning based on responses, providing a realistic simulation.

How we built it

Case-GPT was built using natural language processing (NLP) techniques and open artificial intelligence models, leveraging Meta's LLama-2 model fine-tuned and prompt engineered for casing interviews. The frontend interface utilized web technologies like HTML, CSS, and JavaScript, while we used Endpoint API calls to the LLM using together-ai for scalability.

Challenges we ran into

Preparing the LLModel to simulate human interviewer behavior presented a significant challenge. Designing training data and ensuring relevant question generation were key tasks. Integrating frontend and backend components, particularly handling real-time interactions, posed technical challenges. The biggest challenge was with pivoting to Open sourced models after being repeatedly rate limited by Azure OpenAI models as our initial idea was to build with GPT and Whisper for a real interview experience.

Accomplishments we're proud of

We're proud to have developed a semi-sophisticated AI platform offering a realistic environment for practicing casing interviews. Case-GPT enables users to gain confidence and proficiency in addressing complex business problems. The seamless integration of AI technology and user interface design resulted in a polished and user-friendly experience, and people can now practice casing interviews which specifically require people to sit with another person to practice.

What we learned

Developing this project provided valuable insights into natural language processing and machine learning technologies. We learned to effectively leverage pre-trained language models and fine-tune them for specific applications. Experience in frontend and backend development, LLMs, and user experience design was also gained.

What's next

for case-gpt Future plans for Case-GPT include expanding case study availability - particularly so by finetuning the LLM to learn from being given a case then framing questions by itself, and integrating additional learning resources. Collaboration with consulting firms and educational institutions aims to incorporate Case-GPT into training programs, benefiting more individuals seeking effective interview practice. In our analysis of this market, there were a handful of companies trying to do something similar but they are not scaled enough that their chatbot self learns while interacting with the user.

Analysis

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Technology

Found in codeClaimed only
  • PythonIn code
  • JavaScriptClaimed
  • Next.jsClaimed

1 of 3 appear in the indexed code. 2 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

11 KB

Source files

5

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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