Summary
Keeping large content repositories organized is an ongoing challenge. There's always new stuff coming in, and taxonomies evolve over time. Resource-strapped teams seldom have opportunities to re-categorize older content. It's a task well-suited for generative AI. Large language models have powerful capabilities that can help teams keep content organized at scale. Using LLMs in this capacity can lead to better user experiences and free team members to focus on more valuable efforts. This presentation explores two approaches for using LLMs to organize content at scale: 1) re-categorizing content using existing categories and 2) developing new categories from existing content. Both will be shown as proofs of concept alongside feasible next steps.
Key Insights
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Large information systems require ongoing organization to remain effective and user-friendly.
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Search functionality is often inadequate for large content repositories; context and categorization matter.
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AI, particularly language models like GPT-4, can greatly improve the organization of content at scale.
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There are two types of content repositories: user-facing and internal knowledge repositories, each needing tailored organization strategies.
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Taxonomies need to evolve alongside changing product offerings and industries.
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Retagging old content is tedious but can be automated using AI, significantly reducing time spent on manual categorization.
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AI can help identify themes and improve visibility for older, evergreen content that is otherwise overlooked.
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Implementing AI-driven content organization requires a clean and standardized taxonomy for best results.
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Methods such as Retrieval Augmented Generation (RAG) enhance LLM capabilities by integrating relevant content dynamically.
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Engaging hands-on with AI tools is essential for professionals working in content management and information architecture.
Notable Quotes
"Keeping large content repositories organized is an ongoing challenge."
"Content that is not prioritized becomes less usable and therefore less useful."
"AI can help organize content very effectively, both faster and at larger scales than humans can."
"I decided to implement a 'see also' feature on my blog to link related posts."
"The process reduced the time that this activity took significantly."
"These tools have tremendous potential for organizing large content repositories."
"We should think of these as tools to augment human work and let us focus on what matters most."
"Tools like RAG allow for precise querying of content repositories without compromising on specificity."
"The big takeaway for me is that these tools are not just about doing things faster; they open up new methods of working with content."
"It's important to learn about these technologies hands-on and find ways to apply them to our work."
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