Why Your AI Models Are Living in the Matrix (And How RIG Gets Them Out)

[RIG = Retrieval Interleaved Generation using LLM]

Ever wonder why that AI model that was exceptional and genuinely intuitive six months ago is now increasingly making questionable decisions? Welcome to the Matrix.

AI Decay of Search Engines/News Feeds. You search for the best phone this year, but the top results are from 2022! The search suggestions show models that are now discontinued! And the news feed keeps showing you articles about topics you read three (3) months ago, but doesn’t show the major breaking news happening today. 

AI decay can involve data drift, concept drift and other factors. Data drift happens when the AI model trained on 2024 customer behavior is now facing 2025 humans who’ve evolved their language, preferences, and patterns.

When data drift is corrected there is less of a struggle to interpret new conversational patterns. The training data and reality have merged. 

Fashion retailer algorithms face this constantly. A model trained on fall trends is recommending items users have already moved past. By the time you notice declining click-through rates, you’ve lost weeks of revenue. 

Concept Drift: The emergence of the anomaly, similar to the movie, The Matrix there was a fatal flaw. Its core assumption that humans would accept a perfect world was wrong. Our similar reality with AI models is when a fraud detection model assumed payment patterns would remain stable. Then cryptocurrency payments surged in early 2025.

Buy-now-pay-later services evolved. Digital wallets changed transaction flows.

Like Neo breaking the rules, real-world changes have invalidated the AI model’s understanding of reality itself. Social media algorithms trained on traditional posts suddenly faced AI-generated content, hybrid media types, and short-form video explosions. The foundational concepts became incorrect overnight. 

Voice AI assistants showcase this perfectly. Early 2025 models were designed for command-response interactions. Users in 2026 expect multi-turn conversations with emotional intelligence. The concept of what an assistant should do has fundamentally shifted. 

Drift, for example, is also when customer service chatbots hit a wall regularly. Patching and updating only goes so far. Eventually the entire conversational framework needs rebuilding to match how people actually communicate now. 

Retrieval Interleaved Generation (RIG) is a recent addition. Traditional AI models are static snapshots. They’re frozen in time. But RIG (Retrieval Interleaved Generation) changes that to help keep up with real-time data. RIG allows AI to see beyond its training. Instead of being trapped in a static simulation, RIG-powered AI models stay updated with current events. They pull live data so that their responses can be relevant in the now. They’re not just regurgitating training data, they access current information about today’s stock prices, yesterday’s sports scores, or this morning’s tech announcements. 

How RIG Helps When AI Systems Need Major Updates 

RIG changes how we fix outdated AI systems. Think of it like this: when your phone’s operating system gets too old, you usually have two choices. You can buy a completely new phone, or you can update the software. RIG is like having a smart update system that keeps your old phone working with new apps without replacing the whole device.

Normally, when an AI system becomes outdated, companies have to rebuild it from scratch. That’s expensive and time-consuming, like tearing down a house and building a new one. RIG provides a shortcut. It acts as a connector between the old AI and current information, so the AI can still give up-to-date answers without being completely retrained.

When a major update does become necessary, RIG makes it cheaper and easier. The AI keeps the useful stuff it already learned, while RIG handles all the new information and changing trends. This means companies can keep their AI systems running longer before needing expensive overhauls. Instead of massive rebuilds every six months or year, RIG allows the system to evolve gradually. The retrieval part absorbs most of the changes, while the main AI stays stable and keeps working.

This essay was inspired by Deborah Levine’s opinion column:  “AI IS SCARY, BUT MORE HUMAN THAN YOU THINK.” Levine was recently hired to help humanize AI involving the use of the Retrieval Interleaved Generation (RIG).