Did you know that over 70% of AI hallucinations come from poor retrieval... not from the model itself? This is one of the biggest misconceptions in AI today. OpenAI recently published new guidance on improving factual accuracy in LLM answers. One key takeaway: Most hallucinations actually come from bad retrieval. People assume LLMs “make things up” at random, but in reality, most errors happen because the system can’t pull the right data in the first place. That’s why retrieval quality is becoming the most important factor in LLM optimization. Here are three practical levers that directly improve retrieval accuracy: 1️⃣ Structured content → Use clear headings, lists, and schema so LLMs can parse your data. 2️⃣ Entity clarity → Make brand names, product terms, and key concepts unambiguous. 3️⃣ Context linking → Interlink related content so AI understands relationships between concepts. When these are in place, LLMs are more likely to fetch your data accurately, which reduces hallucinations and improves AI search visibility. This is exactly what OpenAI’s paper highlights: accuracy isn’t only about stronger models, but about optimizing how knowledge is retrieved and represented. That is what LLM Optimization revolves around and what Algomizer specializes in.

@algomizercom Crazy to think getting the right data in is way more important than just having a strong model

“We have tacitly abandoned certain public spaces to the most disordered and depraved among us because enforcing the law feels mean and makes us uncomfortable,” writes @katrosenfield.