Mar 17 · 18 min read · When I last wrote about this project, I was benchmarking enterprise AI inference tooling against a local alternative on cutting-edge GPU hardware — and discovering that enterprise frameworks are not a
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Mar 8 · 8 min read · TL;DR Retrieval-Augmented Generation (RAG) systems — used by ChatGPT plugins, Copilot, and enterprise LLMs — leak document identities and content through embedding fingerprints and model inversion attacks. An attacker can reconstruct your proprietary...
Join discussionFeb 14 · 5 min read · You built a Retrieval Augmented Generation (RAG) system. It works beautifully on small documents. Answers are grounded, citations look clean, and latency is reasonable. Then someone uploads a 300 page PDF. Suddenly: Answers mix unrelated sections C...
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Feb 12 · 9 min read · The Problem with Vanilla RAG You have built a RAG system. It works great for simple questions, but then someone asks: How does Anthropic's approach to AI safety differ from OpenAI's? What are the implications for the industry? In such a case, your sy...
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Feb 8 · 5 min read · Why modern RAG systems struggle with visually rich documents—and how late-interaction retrieval helps. Modern AI systems don’t just search through plain text anymore. They search through PDFs, scanned documents, charts, tables, infographics, and imag...
Join discussionFeb 5 · 9 min read · What is Retrieval-Augmented Generation (RAG)? Retrieval augmented generation is a process that provides a large language model (LLM) with domain specific and relevant context retrieved from an external knowledge base to help it answer queries more ac...
Join discussionJan 23 · 4 min read · Large Language Models like those powering GPT, Gemini, and Anthropic do wonders when prompted to generate images, videos, and text. But they struggle to provide accurate data when asked about a niche subject. If you ask LLM (Large Language Model), “H...
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