Three Competing Approaches to Keep LLMs Knowledge Fresh and Accurate

Large language models inherently struggle with real-time information and topics beyond their training data, leading to hallucinations. This article explores three complementary approaches—fine-tuning, prompt engineering, and RAG—to extend LLM capabilities, each with distinct trade-offs between customization, speed, and knowledge freshness. Understanding these methods is critical for developers building production AI systems that require accurate, up-to-date information.