Retrieval-Augmented Generation News Retrieval-Augmented Generation (RAG) is quickly becoming the “accuracy layer” for generative AI in journalism. By pairing LLMs with real-time retrieval over trusted archives and live feeds, news teams can ship faster summaries, build reader-facing chatbots, and power editorial copilots-without betting everything on static model memory. This long-form technical deep dive covers recent developments in RAG for news: major adopters, emerging frameworks, retriever innovations, latency + indexing improvements, and the governance patterns that help prevent hallucinations. Get in Touch Introduction: AI, News, and the Rise of RAG The news industry faces information overload, 24/7 publishing pressure, and
Author: Dino Cajic
What Is RAG in AI? RAG (Retrieval-Augmented Generation) is a technique that helps AI systems answer more accurately by letting them retrieve trusted information before they respond. This guide explains what RAG stands for, why it exists, how it works step-by-step, and where it’s used in the real world. Jump to How RAG Works What Does RAG Stand For in AI? In AI, RAG stands for Retrieval-Augmented Generation. It combines a generative AI model (the part that writes answers) with an external information source (the part that can “look things up”). Instead of relying only on what the model
Privacy-Preserving AI Techniques Differential privacy, federated learning, and homomorphic encryption are three practical ways to train or use AI on sensitive data without exposing personal information. This guide explains how each technique works (in a moderately technical way), where it fits, and real-world examples you’ve probably already used. Start Here Why privacy-preserving AI matters AI gets dramatically better with real-world data — medical records, customer support logs, transaction histories, location traces, and on-device behavior. The problem: centralizing or exposing that data increases breach risk and can violate privacy expectations (and regulations). Privacy-preserving AI is the toolbox that lets you
What Is Agentic AI? Imagine telling your digital assistant, “Help me plan a trip to Everest next spring.” A traditional AI might return information. Agentic AI can go further: it can plan, decide, and take actions (like booking flights and hotels) with minimal supervision. In plain English, if you’re asking “what is agentic AI?” the answer is: it’s AI that behaves like a goal-driven agent, not just a text generator. Get in Touch Agentic AI, explained in plain English At its core, agentic AI is an advanced form of artificial intelligence focused on autonomous decision-making and action. The word
The AI Bubble: Hype vs Reality AI is simultaneously the most important technology shift of this decade and the most over-marketed. Venture funding is concentrating into AI, valuations are stretching, and compute spending is exploding. This deep dive separates what’s real from what’s hype – and explains why some critics point to “circular investment” loops (especially around NVIDIA) as bubble logic. Get in Touch What People Mean When They Say “AI Bubble” A bubble isn’t “a technology is useless.” A bubble is when expectations and capital allocation run far ahead of near-term business reality – creating valuations and spending
AI Safety News (2023–2025): What’s Real, What’s Theater AI safety isn’t a “future problem” anymore. It’s a shipping-and-liability problem, a governance problem, and (increasingly) a national-security problem. In this article, we’ll walk through the biggest recent AI safety developments, why a lot of “safety work” still fails in practice, and what tech teams can do to build real defense-in-depth instead of security theater. Get in Touch Introduction AI safety has moved from an academic concern to a front-page issue for the tech industry and policymakers. Over the last two years, global summits, regulatory moves, expert warnings, and model “missteps” have
Monitoring AI Systems in Production Production models don’t just “break” – they drift. This technical guide shows how to continuously monitor AI behavior (LLMs, computer vision, recommenders, classic ML) for data drift, prediction drift, outliers, and performance regressions that can also be early signs of security issues. Jump to the Checklist Why continuous monitoring matters AI systems are “live” in a way traditional software isn’t. Input data changes, user behavior changes, upstream pipelines change, and threat actors adapt. If you’re not watching inputs, outputs, and system health continuously, quality can degrade quietly – and the same signals that indicate
Open-Source Model Risks and Trust Open-source AI models and datasets can dramatically accelerate development, but they also expand your attack surface in ways that feel different from “normal” software dependencies. This page walks through the upsides, the real risks (including backdoors and poisoned artifacts), and a practical intake workflow: rigorous vetting, checksums, authenticity verification, and community trust signals so you can ship faster without gambling on integrity. Jump to the Checklist Why developers reach for open-source models + datasets Open-source gives you leverage: strong baselines, fast iteration, and the ability to self-host and customize. It’s not just “free as
Defending Against Data Poisoning AI models learn from whatever data we feed them – and that’s a double‑edged sword. Training data poisoning is when someone sneaks malicious or misleading examples into your training set so the model quietly learns the wrong lessons. The scary part: poisoned data often doesn’t look “obviously malicious.” Even a tiny contamination (think: fractions of a percent) can meaningfully shift behavior, insert hidden backdoors, or degrade outputs in ways that only show up after deployment. Get in Touch What Is Training Data Poisoning? Training data poisoning means tampering with the data a model learns from
Secure Model Deployment & MLOps Shipping ML to production isn’t “just another microservice.” You’re exposing valuable IP (the model), sensitive data paths, and a compute endpoint that can be abused. This guide breaks down a practical, CTO-friendly blueprint for preventing unauthorized access, model tampering, data leakage, and operational surprises. Request a Security Review Why secure ML deployment is different Traditional app security is necessary, but it’s not sufficient for model-serving. The model itself is an asset, and the ML lifecycle introduces new attack surfaces (training pipelines, artifacts, dependencies, drift, and feedback loops). Here are the failures that actually hurt