How RAG Prevents AI Hallucinations: Verified Responses

The Problem: Hallucinations in Pure LLMs

Large language models like ChatGPT, Gemini, or Claude are trained on billions of tokens of data. When asked something that doesn't appear exactly in their training, **they invent convincing answers that sound correct but are completely false**. This is called a hallucination.

Real example: Ask ChatGPT "What is article 17.5 of the LPH?" and it could invent a majority or protection that doesn't exist. For a property manager, this is a legal disaster.

The Solution: RAG Enforces Verification

RAG completely changes the flow:

1. Fact Search: First, search for article 17.3 in your legislation database
2. Literal Extraction: Extract the exact text from the original PDF or document
3. Guided Generation: The LLM only generates responses based on that document, without inventing
4. Mandatory Citation: Always includes the source: "Article 17.3, Page 45, Paragraph 2"

Real Example: LPH in Igera

Question: "How many owners are needed to approve an elevator?"

RAG responds: "According to Article 17.2 of the Horizontal Property Act, a simple majority of owners present at the meeting is required. Source: Official State Gazette, LPH 1960, page 3."

Every word you see comes from the actual document. Zero hallucinations, zero risk.