Over the last twenty years, legal professionals have filled the web with blogs, client alerts, white papers, and more. This digital publishing created a massive body of secondary law.
However, this publishing was built for a human-centric, URL-driven web. While web links and RSS feeds allowed us to browse and cite, they suffer from two major vulnerabilities: Link rot by broken or moved urls and fragmentation where this publishing is in siloed platforms instead of a unified knowledge base.
In an AI world where users turn to LLMs (Claude, Gemini, ChatGPT) and AI driven solutions for information, unstructured web pages and your scraped publishing aren’t enough. Legal publishing needs to be vectorized.
Vectorization is the process of converting text into dense numerical representations that capture semantic meaning rather than keyword matches.
There are significant advantages with vectorized data.
- Semantic Discovery: Traditional search misses insights when the words don’t match. Vectorized content looks at intent and substance, matching a user’s real-world scenario directly to the core issue a legal professional’s publishing might be addressing.
- By organizing legal professionals’ published work so AI can find and use it, we can bring their insight into the tools people already use. A lawyer working on a transaction or someone trying to understand a legal issue could receive relevant analysis from a knowledgeable legal professional, with a citation to the published work. Technologies called RAG and MCP help make those connections possible.
- Preservation: Storing published works as durable vectors tied to permanent author records instead, of shifting URL’s ensures one’s legacy and a preservation of secondary law.
Legal professionals learned what the Internet, websites and URLs were. We’ll become familiar with vectorizing, LLMs, RAG and MCP, too. What matters is how these technologies help your insight reach and serve people and that you establish authority in the process..