Privacy Controls for RAG: Row-Level Security and Redaction Before LLMs
RAG systems often leak sensitive data because they give LLMs full access to internal documents. Row-level security and data redaction before the AI sees the data are essential to prevent breaches, comply with regulations, and protect customer trust.
Prompt Injection Risks in Large Language Models: How Attacks Work and How to Stop Them
Prompt injection attacks trick AI models into ignoring their rules, exposing sensitive data and enabling code execution. Learn how these attacks work, which systems are at risk, and what defenses actually work in 2025.
Regulatory Frameworks for Generative AI: Global Laws, Standards, and Compliance
Global regulations for generative AI are now active in the EU, China, California, and beyond. Learn what laws apply to your AI tools, how compliance works, and what steps to take now to avoid fines and legal risks.
Accessibility Risks in AI-Generated Interfaces: Why WCAG Isn't Enough Anymore
AI-generated interfaces are breaking accessibility standards at scale, leaving millions of users behind. WCAG wasn’t built for dynamic AI content-and without urgent changes, algorithmic exclusion will become the norm.
Disaster Recovery for Large Language Model Infrastructure: Backups and Failover
LLM disaster recovery isn't optional anymore. Learn how to back up massive model weights, set up failover across regions, and avoid the top mistakes that cause costly outages in AI infrastructure.
Rotary Position Embeddings (RoPE) in Large Language Models: Benefits and Tradeoffs
Rotary Position Embeddings (RoPE) have become the standard for long-context LLMs, enabling models to handle sequences far beyond training length. Learn how RoPE works, why it outperforms traditional methods, and the key tradeoffs developers need to know.
Grounded Web Browsing for LLM Agents: How Search and Source Handling Power Real-World AI
Grounded web browsing lets AI agents search live websites for real-time info, fixing outdated answers. It's now powering enterprise tools with 72%+ accuracy-but comes with high costs, technical hurdles, and big ethical questions.
How Analytics Teams Use Generative AI for Natural Language BI and Insight Narratives
Analytics teams are using generative AI to turn data questions into instant, narrative-driven insights. Natural language BI lets anyone ask questions in plain English and get clear explanations-no coding needed. Here’s how it works, who’s using it, and what you need to know.
Speculative Decoding for Large Language Models: How Draft and Verifier Models Speed Up AI Responses
Speculative decoding accelerates large language models by pairing a fast draft model with a verifier model, cutting response times by up to 5x without losing quality. Used by AWS, Google, and Meta, it's now standard in enterprise AI.
Best Visualization Techniques for Evaluating Large Language Models
Discover the most effective visualization techniques for evaluating large language models, from bar charts and scatter plots to heatmaps and parallel coordinates - and learn how to avoid common pitfalls in model assessment.
Data Privacy and Compliance Pitfalls for Non-Technical Vibe Coders
Non-technical vibe coders are building apps fast-but often ignoring data privacy laws like GDPR and CCPA. Learn the hidden risks, common mistakes, and simple fixes to avoid fines and protect user trust.
