Prompt Engineering Playground

This project is for experimenting with prompt engineering techniques for Large Language Models (LLMs) using the Gemini API.

Structure

src/: Python source code
- main.py: Main script to run experiments.
- prompt_tech/: Core modules for the project.
  - api.py: Handles interaction with the Gemini API.
  - runner.py: Manages the execution of experiments and saving results.
prompts/: Store prompt templates and examples in text files.
data/: Input data for your experiments (e.g., CSV files).
results/: Output of your experiments (e.g., JSON files with model responses).
tests/: Tests for your experiment code.

Install dependencies:
```
uv pip install -r requirements.txt
```
Set up your environment variables:
- Create a .env file in the root of the project.
- Add your Gemini API key to the .env file:
```
GEMINI_API_KEY="YOUR_API_KEY"
```

Add your prompts:
- You can add new prompts and techniques to the prompts dictionary in src/main.py.
- For more complex prompts, you can save them as text files in the prompts/ directory and read them in your code.
Run the experiments:
```
python src/main.py
```
Analyze the results:
- The results of the experiments will be saved in results/experiment_results.json.
- This file will contain the prompt, the model's response, the technique used, and the latency for each experiment.