Limitations of Static LLM Data in AI Applications

“There will be more AI Agents than people in the world.” – Mark Zuckerberg As AI grows, autonomous agents powered by LLMs (large language models) take on critical tasks without human oversight. While these systems hold incredible potential, they also face significant risks: manipulation through biased data, unreliable information retrieval, and prompt engineering, all of which can result in misleading outputs. At Chaos Labs, we’ve identified a critical risk: AI agents being unknowingly trained on manipulated, low-integrity data. The result? A dangerous erosion of trust in AI systems. In our latest essay, I dive deep with Reah Miyara, Product Lead, Model Evaluations at OpenAI. https://lnkd.in/eB9mPQWW Key insights from our essay -> The Compiler Paradox: Trust in foundational systems can be easily compromised. "No matter how thoroughly the source code is inspected, trust is an illusion if the compilation process is compromised." LLM Poisoning: LLMs are susceptible to “poisoning” through biased training data, unreliable document retrieval, and prompt injection. Once biases are embedded, they taint every output. RAG (Retrieval-Augmented Generation): While designed to make LLMs more accurate, RAG can amplify false information if external sources are compromised. Conflicting Data: LLMs don't verify facts—they generate answers based on probabilities, often leading to inconsistent or inaccurate results. Attack Vectors: LLMs can be attacked through biased data, unreliable retrieval, and prompt engineering—allowing adversaries to manipulate outputs without altering the model. The Path Forward -> Trust in LLMs must go beyond surface-level outputs and address the quality of training data, retrieval sources, and user interactions. At Chaos Labs, we’re actively working on solutions to improve the reliability of AI systems. Our vision for the future is simple: With GenAI data exploding, verified truth and user confidence will be an application’s competitive edge. To get there, we’re developing solutions like AI Councils—a collaborative network of frontier models (e.g., ChatGPT, Claude, LLaMA) working together to counter single-model bias and enhance reliability. If these challenges excite you, we want to hear from you.

You're under pressure to deliver on AI's promise while navigating vendor hype and technical limitations. Your leadership team wants ROI, your employees want tools that work, and you're desperately trying to separate AI reality from market fiction. And now, you're learning the news that the AI foundation everyone's building on was never solid, and research shows it's actively getting worse. Wait... what? Doesn't emerging technology typically improve over time? It's called "model collapse". We've all heard "garbage in, garbage out." This is the compounding of that. LLMs trained on their own outputs gradually lose accuracy, diversity, and reliability. Errors compound across successive model generations. A Nature 2024 paper describes this as models becoming "poisoned with their own projection of reality." But here's the truth. LLMs were always questionable for business decisions. They were trained on random internet content. Would you base quarterly projections on Wikipedia articles? Model collapse just compounds this fundamental problem. What does this mean for your AI strategy, since much it is likely based on the use of LLMs? It comes down to the decisions you make at the beginning. Most of us are rushing to launch the latest model, when we should be looking at what's best for the use case at hand. First things first, deploy LLMs when you can afford to be wrong: ✔️ Brainstorming and ideation ✔️ First-draft content (with human editing) ✔️ Low-stakes support services Stop using LLMs when being wrong carries costs: 🛑 Financial analysis and reporting 🛑 Legal compliance 🛑 Safety-critical procedures I'm not saying LLMs are useless. Agentic AI will be driven by them, but there are significant achievements in small language models (SMLs) and other foundational, open-source models that perform just as well, even better, at particular tasks. So here's what you need to do as part of your AI strategy: 1️⃣ Classify your AI use cases: For all use cases, classify by accuracy required. You can still use LLMs, but that just means you need more validation around outputs 2️⃣ Assess LLM vs. SML strategy: Evaluate smaller, domain-specific language models for critical functions and experiment with them against LLMS and see how they perform 3️⃣ Consider deterministic alternatives: For calculations, and workflows requiring consistency, rule-based solution or deterministic AI solutions may be better 4️⃣ Design hybrid architectures: Combine specialized models with deterministic fallbacks. This area is moving fast; flexibility is key The bottom line? Your success will be measured not by how quickly you adopt every AI tool, but by how strategically you deploy AI where it creates value and reliability. Model Collapse Research: https://lnkd.in/gUTChswk Signs of Model Collapse: https://lnkd.in/g5ZpAk89 #ai #innovation #future                                

Can AI models get "Brain Rot"? New research says, Yes! A recent paper on the 'LLM Brain Rot Hypothesis' presents findings that are crucial for anyone involved in AI development. Researchers have discovered that continuous exposure to low-quality web content leads to lasting cognitive decline in large language models (LLMs). The key impacts identified include:- - 17-24% drop in reasoning tasks (ARC-Challenge). - 32% decline in long-context understanding (RULER). - Increased safety risks. - Emergence of negative personality traits (psychopathy, narcissism). What defines "junk data"? Two dimensions are significant:- - Engagement-driven content (short, viral posts). - Low semantic quality (clickbait, conspiracy theories, superficial content). The most concerning finding is that the damage is persistent. Even scaling up instruction tuning and clean data training cannot fully restore baseline capabilities, indicating deep representational drift rather than mere surface-level formatting issues. This research highlights that as we develop autonomous AI systems, data quality transcends being a mere training concern; it becomes a safety issue. We need to implement:- - Routine "cognitive health checks" for deployed models. - Careful curation during continual learning. - A better understanding of how data quality affects agent reliability. The paper emphasizes that data curation for continual pretraining is a training-time safety problem, not just a performance optimization. For those building production AI systems, this research should fundamentally alter our approach to data pipelines and model maintenance. Link to paper: https://lnkd.in/drgjvt8a #AI #MachineLearning #AgenticAI #DataQuality #AIResearch #LLM #AIEthics