Failure-Bounded Node.js Topic Classification with Semantic Search and LLM Eviden

<!-- SC_OFF --><div class="md"><p>We spent the last year building a code indexing system and tried pretty much every retrieval approach along the way. Sharing what actually worked

<p>Short answer: define moderation categories for a startup app as seven risk checks, but do not let a category decide the outcome by itself; combine harassment, sexual content,

<p>Short answer: for an e-commerce backend that scores job candidates against a rubric, I would choose the simplest API that can hold a JSON contract, expose cost per tenant, and

<p>A semantic search system has already crossed its security boundary before generation begins: if retrieval admits another customer's chunk, no prompt can make that access

<p>In many enterprise workflows, classification sounds simple.</p> <p>An email arrives.<br /><br /> A ticket is created.<br /><br /> A request needs to be routed.</p> <p>At first

<p>Most semantic search systems don’t fail because of embeddings. They fail because of how the system is designed around them.</p> <p>When I started building a semantic search API

<p>Every RAG prototype wants a vector database before it wants a data model.</p> <p>That is usually backwards.</p> <p>The first hard question is not which ANN index has the nicest

<h1> Beyond Vector Search: Why GraphRAG is the Next Frontier for LLMs </h1> <p>For the past year, the industry standard for augmenting LLMs has been <strong>Retrieval-Augmented