How AI Apps Actually Use LLMs: Introducing RAG

If you’ve been exploring AI applications, you’ve probably come across the term RAG . It appears everywhere - chatbots, AI assistants, internal knowledge tools, and documentation search. But before understanding how it works, it helps to understand why it exists in the first place. Large language models are powerful. However, when used on their own, they have a few fundamental limitations. ⚠️ Problems With LLMs On Their Own LLMs are impressive — until they start failing in real-world scenarios. 1. Outdated Knowledge Every model has a training cutoff date. If asked about something that happened after that point, the model may: say it doesn't know generate an answer that sounds plausible but is incorrect 2. Hallucinations LLMs do not know things in the traditional sense. They generate text by predicting what is most likely to come next based on patterns in training data. When the correct information is missing, the model may still produce a confident-sounding but incorrect answer. That be