\n In this video, I extend the RAG project into a concrete product: a semantic search engine integrated into WordPress via a custom plugin, fully built with Claude Code.<\/p>\n
Instead of limiting the RAG to a chatbot, I reuse its research corpus to create a powerful semantic search layer on top of WordPress content.<\/p>\n
The idea is simple:
\n– Extract and structure the RAG knowledge base
\n– Index it using embeddings
\n– Enable semantic search directly inside WordPress<\/p>\n
A practical use case:
\nAn HR Director filtering CVs against job descriptions using semantic matching instead of keyword search.<\/p>\n
In my case, I apply it to content and articles, enhancing WordPress native search with AI-driven relevance.<\/p>\n
This video covers:
\n– Extracting the RAG corpus
\n– Designing a semantic search pipeline
\n– Building a WordPress plugin powered by embeddings
\n– Extending WordPress search capabilities with AI<\/p>\n
This demonstrates how a RAG project can evolve into multiple usable products beyond chat.\n <\/p>\n","protected":false},"excerpt":{"rendered":"
In this video, I extend the RAG project into a concrete product: a semantic search engine integrated into WordPress via a custom plugin, fully built… <\/p>\n