Experimenting with AI for Metadata Trends: Training Models, Not Just Testing Extraction

AI is already capable of extracting metadata, identifying patterns, and analyzing trends over time—the real challenge isn’t proving that AI can do these things. Instead, the challenge is in training AI frameworks to do them efficiently, accurately, and at scale for real-world applications.

That’s what I’ve been experimenting with—not testing whether AI can perform metadata extraction, but fine-tuning how it does it so that when organizations need it, they aren’t starting from scratch. Instead, they’ll have a tested framework ready to deploy.

One of the more interesting questions that has come up in this process is: Can metadata actually reveal how trends evolve over time?

At first, it seems like metadata is static—titles, dates, authors, keywords. But when analyzed across decades of documents, metadata can highlight shifts in language, policy, and priorities that researchers might otherwise miss.

For example:
📜 A 1980s zoning document might refer to “urban renewal.”
📑 A 2020s planning document might use “affordable housing.”

AI can be trained to detect these shifts in terminology and highlight patterns across time, giving researchers a new way to track how policies and ideas have changed. However, within this context, to get there, AI needs to accomplish 3 things:

1. Extract surface metadata (title, date, author, etc.).
2. Extract hidden or embedded metadata (from images, diagrams, and handwritten notes).
3. Analyze how keywords and themes change over time.

Another challenge in this experiment is getting multiple AI systems to work together.

• Gemini 2.0 is strong at analyzing images and extracting hidden metadata.

• OpenAI is great at processing text-based metadata and identifying trends in language.

By combining both, the goal is to align their outputs so that metadata from images (extracted by Gemini) and metadata from text (extracted by OpenAI) can be merged into a single framework. That’s where testing comes in—fine-tuning how AI extracts, compares, and connects metadata across different sources.

For organizations working with large-scale metadata collections, this kind of testing is critical. It’s not about whether AI can do the job—it’s about making sure it’s done right, consistently, and in a way that’s actually useful.

If you have questions or you're working with large-scale digital collections, I'd love to hear how you're thinking about AI-powered research automation! 🚀