# Product Context: PyNamer

**Problem:** Manually naming large numbers of image files is tedious and time-consuming. Generic filenames (e.g., `IMG_1234.JPG`) lack descriptive value, making it hard to find specific images later.

**Solution:** `pynamer` automates the process of generating descriptive filenames for images by leveraging the image understanding capabilities of multimodal LLMs.

**User Experience:**
- The user provides one or more image paths via the command line.
- The tool processes each image, interacts with an LLM (configured via `config.yaml`), and renames the file with a descriptive, clean filename.
- A dry-run option allows users to preview the changes without modifying files.
- **Efficiency Enhancement:** By resizing large images before sending them to the LLM, the tool aims to:
    - Reduce the amount of data transferred.
    - Potentially lower API costs (as some models charge based on input size/tokens).
    - Speed up the processing time.

**Target User:** Individuals or teams dealing with many images who need a better way to organize and retrieve them based on content (e.g., photographers, researchers, content creators).