3 posts tagged with "RAG"

Open your analytics and look at what people type into your AEM site's search box. Not the page views: the queries. It's the only place on your site where visitors tell you, in their own words and unprompted, what they came for.

Now look at what you give them back: ten blue links, ranked by whether their words happened to appear in your authors' words. If they used a synonym, a typo, or a full sentence, they got nothing, and then they left, and no one filed a ticket about it.

That gap is the subject of the page we just published: turing.viglet.org/aem-search: AEM search, reimagined with AI, without the ceiling. This post is the longer version: not the feature list, but what actually becomes possible on an AEM site when search stops matching strings and starts understanding questions.

Retrieval-Augmented Generation (RAG) is only as good as what it retrieves. If the retrieval layer is a throwaway vector index built for one chatbot, you end up rebuilding it for every new use case. The better pattern: make your enterprise search index the retrieval layer, and expose it to AI through the Model Context Protocol (MCP) so any agent can use it as a tool.

That's what Viglet Turing does: your indexed Adobe AEM, WordPress, database, and file content becomes both a search experience and a grounded RAG/agent capability, on your own infrastructure.

Once your Adobe Experience Manager content is indexed into an enterprise search engine, the obvious next step is to move beyond keyword matching. A visitor who searches "places to stay near the mountains" should find a page titled "Alpine lodges and cabins", even though none of those words match. That's semantic search, and it's powered by embeddings.

This post builds on indexing AEM into Viglet Turing and shows how to add vector/semantic search and RAG on top, all open source, on your own infrastructure.

For the overview of AEM search with Viglet Turing, with a live demo, see turing.viglet.org/aem-search.