RAG - Vector DB

<p>Your RAG application works perfectly with 1,000 documents. You push it to production, upload 10 million vectors, and suddenly your query latency jumps from 50ms to 5 seconds.

<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>Our careers page search often felt broken. Searching for “Java” could return JavaScript roles. A misspelling could produce no results at all. Searching for “manager” could

<!-- 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

<h1> Moving Beyond Simple Vector Search: Why Hybrid Search is Essential for RAG </h1> <p>As LLMs continue to dominate the landscape, Retrieval-Augmented Generation (RAG) has

<blockquote> <p>Originally published on <a href="https://freedevkit.com/blog/vector-embeddings-the-foundation-of-semantic-ai/" rel="noopener noreferrer">FreeDevKit</a>.</p>

<p><em>Originally published on <a href="https://buildwithaws.substack.com" rel="noopener noreferrer">Build With AWS</a>. Subscribe for weekly AWS builds.</em></p> <p><a

<h1> What Are Vector Embeddings? (And Why Your Spotify Wrapped Knows You Too Well) </h1> <p>Imagine a postal worker who never learned to read. Not a single word. Can't tell an A