Why You're Using LLMs Completely Wrong

Stop relying on your LLMs or autonomous agents to recall facts from their training data. Foundation models shift too fast, and your local data is constantly updating. Instead, implement this mental model: treat your LLM strictly as a reasoning engine and force all factual data to flow through a dedicated retrieval pipeline (RAG). By enforcing this clear separation of concerns, you immediately wipe out an entire class of hallucination errors. Resources: 🔬 Explore Open-Source Observability with Phoenix: https://phoenix.arize.com 🔗Get Started with Arize AX: https://arize.com 📖 Read the Developer Documentation: https://docs.arize.com Subscribe for more production-grade AI infrastructure and observability frameworks from Arize AI! 🚀 : https://www.youtube.com/@arizeai?sub_confirmation=1 #AIEngineering #RAG #AIAgents