Built with Google Gemini: AI Essay Generator with Gemini

This is a submission for the Built with Google Gemini: Writing Challenge

What I Built with Google Gemini

1. This Python script leverages the Google Gemini API to automate the generation of essays based on specific criteria defined in a CSV file. It's engineered to create datasets of student-like essays, complete with a specified type and percentage of errors, making it ideal for training NLP models, developing educational software, or generating content at scale.
2. The script intelligently infers the desired error type (e.g., spelling, grammar, punctuation) from the input CSV's filename, generates a unique essay for each row, and saves the output directly back into the same file, creating a seamless and efficient workflow.

Demo

I've uploaded my project to GitHub named gemini-essay-generator

What I Learned

• Technical Skills

  • Gained hands-on experience with Python scripting and data handling using pandas for reading/writing CSV files.
  • Learned to interact with the Google Gemini API, including constructing prompts dynamically and handling API responses.
  • Practiced error handling and validation, ensuring the generated essays met word count and error constraints.
  • Understood dependency management in Python projects and how a requirements.txt ensures reproducible environments.

• Soft Skills

  • Improved problem-solving and debugging when working with APIs and CSV-based data.
  • Learned to manage project structure, separate configuration (API keys, CSVs) from logic, and read/interpret open-source code.
  • Strengthened attention to detail, especially while ensuring generated essays had the required type and percentage of errors.

• Unexpected Lessons

  • Saw firsthand how automated AI generation can still require validation and retries — AI outputs are not always deterministic.
  • Learned that file naming conventions can be leveraged to encode metadata (like error types) in a simple, scalable way.
  • Realized the importance of lockfiles and reproducible environments; without them, collaborators may get inconsistent results.

• Key Takeaway

  • Working on this project highlighted the combination of data engineering, API integration, and reproducibility in real-world Python projects, and how thoughtful automation can save significant time when generating content at scale.

Google Gemini Feedback

• What Worked Well

  • The project structure was clear, with a single main script and example CSV that made it easy to understand how input/output worked.
  • API integration with Google Gemini was straightforward; the existing functions for sending prompts and receiving responses worked reliably.
  • Using pandas for CSV handling made batch processing simple and efficient, even for multiple essays at once.
  • The filename-based configuration (to encode error type) was a clever, lightweight solution that avoided complex configuration files.

• Challenges / Where I Needed More Support

  • Initially, understanding how the Gemini API expected prompts and formatting took some trial and error; the API documentation assumed familiarity with prompt engineering.
  • Handling edge cases with essay generation was tricky — sometimes the model returned fewer words than expected or misapplied the error types, requiring multiple retries.
  • The project relied on requirements.txt and pip, so setting up a fully reproducible environment across different systems could be tricky without virtual environments or a lockfile.
  • Some error handling and logging could be more robust; debugging failures when the API response was invalid required manual intervention.

• Key Takeaway

  • Overall, the project worked well for learning AI prompt integration, batch processing, and reproducibility, but highlighted the importance of robust error handling, reproducible environments, and clear API documentation for scaling such tools.

Sure.

Done.