Is RAG already obsolete? It was meant to solve AI’s biggest problems. Instead, it has become one of them.

If you have been following developments in AI search, you have probably heard that RAG is the answer to hallucinations, misinformation and unreliable results. The idea was simple. Instead of relying purely on a language model, AI would retrieve relevant documents first and use them to generate answers. In theory, this meant more factual, grounded responses.

But has it worked? Not really….

AI should work for you, not steal from you.

Every day, AI models scour the internet, vacuuming up everything from research papers to social media posts. They do this without permission, without accountability, and without limits. The result? A chaotic mess of bias, misinformation, and privacy nightmares. And just to salt the wound, you have zero control over it.

But what if you did? What if your AI only worked with your data? What if it never leaked, never trained on someone else’s mistakes, and never exposed sensitive information? Sounds like a task for air-gapped knowledge systems….

If you work in law, privacy is not only a compliance issue, it’s a matter of trust, reputation, and survival.

Your clients hand over some of their most sensitive information, expecting it to stay locked down. Simultaneously, privacy laws are tightening worldwide, and regulators are not messing around.

The days of vague “best practices” are over. If you are not taking active, airtight measures to protect client data, you are not simply risking the fines but could be facing lawsuits, lost business, and damage that takes years to repair.

Privacy laws don’t look the same everywhere. What is legal in one country might get you fined in another. What works for a boutique law firm might not cut it for a global legal powerhouse.

So, what do you actually need to know to keep your firm protected? We’ve broken it down for you.

Ever argued with a chatbot that just did not get it? Or seen a translation so bad it made you laugh? That is Natural Language Processing (NLP) at work, a technology that lets computers ‘understand’ human language, but one that has been on a rollercoaster of breakthroughs and failures.

NLP is behind Google Search, Siri, chatbots, spam filters, and AI-generated textual content, but it has not always been smooth. From the Cold War codebreakers to AI-powered chatbots that convinced people they were sentient, NLP’s history is filled with genius ideas, embarrassing mistakes, and leaps forward that nobody saw coming.

So, how did we go from ancient philosophers dreaming about thinking machines to AI models writing essays? Let’s look into the fascinating evolution of NLP, and why it is still far from perfect.