Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Simon Willison's Blog·Aug 14·5 min read
Don't classify. Hallucinate!
TL;DR
A novel approach to tagging blog content involves prompting a language model (LLM) to generate tags without prior vocabulary, then matching these to existing tags using vector embeddings. This method enhances content classification by leveraging the model's creativity.
✦ Why It Matters
Implement this tagging method to enhance your content classification process today.
Key Takeaways
- Use LLMs to generate novel tags for content.
- Employ vector embeddings to find similar existing tags.
- Enhance content organization with dynamic tagging.
- Improve searchability of blog posts and articles.
How It Works
The method involves prompting a language model to create new tags based on a specific query, such as 'brown coffee table'. The model's output is then analyzed using vector embeddings, which allows for the identification of existing tags that closely match the generated suggestions.
This process enables a more creative and flexible tagging system.