Tagging Blog Posts with BERTopic and LLMs (vickiboykis.com)

A recent project showcased the integration of BERTopic and large language models (LLMs) to enhance blog post tagging, reflecting a growing trend in utilizing AI tools for personal project completion. The author spent 6-10 hours refining the tagging system, leveraging tools such as Claude Code and Pi, and emphasized the importance of user experience in the tagging process. This approach demonstrates the practical applications of LLMs for non-production, personalized projects, where they can effectively aid in content classification and discovery. The significance of this development lies in how LLMs are revolutionizing traditional tagging methods, moving away from labor-intensive processes like Latent Dirichlet Allocation (LDA) towards more sophisticated, context-aware techniques. By using LLMs, the author could generate tags in a zero-shot context, allowing for more relevant categorization based on semantic understanding rather than merely statistical inference. This shift highlights a broader trend where content discovery mechanisms are evolving, particularly as LLMs become integral to content surfacing, potentially diminishing the relevance of traditional tagging systems in favor of AI-driven insights.