The problem with LLMs

Before we build a solution, let's clearly understand the problem. LLMs have three critical limitations that make them unreliable for business applications out of the box.

Limitation 1: Information Cutoff. LLMs are trained on a snapshot of the internet up to a certain date. They don't know about events, menu changes, or policy updates after their training cutoff. If Green Bites changed its hours last week, the LLM has no idea.

Limitation 2: No Private Knowledge. The LLM has never seen your internal documents like kitchen manuals, refund policies, or supplier contracts. It simply cannot answer questions about information it was never trained on.

That approach hits two walls quickly. First, context windows have a token limit, so a 50-page manual simply will not fit. Second, even if it did fit, sending everything is expensive and the model tends to ignore information buried in the middle of a long prompt. You need a smarter way to find and inject only the relevant pieces, which is exactly what RAG does.

Hallucinations are the trickiest because you often cannot tell the answer is wrong just by reading it since it sounds perfectly confident. Knowledge cutoff and private data are "known unknowns" you can plan for, but hallucinations are "unknown unknowns." RAG tackles all three, but hallucination prevention is its biggest win.