6d ago · 3 min read · TL;DR Every vector database used by RAG systems, semantic search, and AI-powered recommendations creates a permanent fingerprint of your data through embeddings. Attackers can use cosine similarity queries to extract original documents, identify user...
Join discussion6d ago · 8 min read · Three months ago I shipped a RAG pipeline that I was genuinely proud of. Semantic search over our internal docs, OpenAI embeddings, Pinecone on the backend. It felt modern. Then someone on our team asked it "what's our parental leave policy?" and it ...
Join discussionMar 5 · 11 min read · The problem with Keyword Search Keyword search breaks more often than most engineers realize. A few months ago, I was building an internal document management tool. Users could upload policy documents
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Mar 5 · 8 min read · Six months ago my team was building an internal support tool for a B2B SaaS company — about 120 employees, docs spread across Notion, Confluence, and a half-dead SharePoint instance from 2019. The ask was simple: a chatbot that could answer questions...
Join discussionMar 4 · 3 min read · Building Production-Ready RAG Applications with Vector Databases Most RAG prototypes look impressive in a notebook. Then they hit production and fall apart. Latency spikes. Retrieval returns irrelevant chunks. Costs balloon when query volume scales. ...
Join discussionMar 3 · 3 min read · Building Production-Ready RAG Applications with Vector Databases Most RAG prototypes look impressive in a notebook. Then they hit production and fall apart. Latency spikes. Retrieval returns irrelevant chunks. Costs balloon when query volume scales. ...
Join discussionFeb 16 · 10 min read · Embedding Model Versioning for Production AI Systems Embedding models power semantic search, recommendation engines, and retrieval-augmented generation systems across modern applications. Yet when you need to upgrade from text-embedding-ada-002 to te...
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Jan 14 · 7 min read · The landscape of Conversational AI is evolving at an unprecedented pace. As we approach 2025, the demand for more intelligent, responsive, and context-aware AI assistants is pushing the boundaries of traditional data management. You've likely experie...
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