16h ago · 5 min read · Building a Retrieval-Augmented Generation (RAG) prototype takes a weekend. Taking that prototype to production without burning through your infrastructure budget is a completely different engineering
Join discussion20h ago · 5 min read · A single, cleverly crafted PDF document can bring down an entire RAG system, hijacking the behavior of AI agents and causing unforeseen consequences. The Problem import PyPDF2 import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer...
Join discussion1d ago · 5 min read · A single, well-crafted adversarial document can manipulate the behavior of an entire AI agent, forcing it to produce malicious outputs without leaving any visible signs of tampering. The Problem import faiss import numpy as np # Create a vector stor...
Join discussion2d ago · 9 min read · By Ravi Mohta A hands-on story about building a local AI document intelligence stack, the promise of NVIDIA NIM, and what the RTX 50 series Blackwell architecture actually means for AI practitioners t
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3d ago · 6 min read · Keywords: context engineering, LLM context management, token efficient prompts, AI agent architecture, LLM workflow optimization Large Language Models (LLMs) are incredibly capable, but many product
Join discussion2d ago · 4 min read · A single maliciously crafted input can bring down an entire Retrieval-Augmented Generation (RAG) pipeline, exposing sensitive data and disrupting critical services. The Problem import numpy as np from sentence_transformers import SentenceTransformer ...
Join discussion3d ago · 19 min read · Most RAG tutorials end the same way: you've got a working prototype and a bill for a vector database that runs whether anyone's querying it or not. Add an always-on embedding service, a hosted LLM end
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