Understanding RAG

How Retrieval Augmented Generation Works

Mode:
The Problem

Traditional LLM Limitations

Large Language Models are powerful, but they have significant limitations that can affect accuracy and reliability.

📅 Outdated
Knowledge frozen at training cutoff date. Cannot access recent information.
🎭 Hallucinations
May generate plausible-sounding but factually incorrect information.
🔒 No Private Data
Cannot access your organization's private documents or databases.
❓ Unverifiable
Cannot cite sources or verify claims against original documents.
📅

Static Knowledge

Trained on data up to a cutoff date. Can't learn new information after training.

🎭

Hallucinations

Confidently generates incorrect facts when unsure. No way to verify accuracy.

🔒

No Private Access

Cannot access your internal documents, databases, or real-time data.

❓

No Sources

Cannot cite references or point to source documents for verification.