RAG is everywhere in modern AI systems.
But most explanations are so abstract they're useless.

Here’s the simple version:

🧠 1. LLMs are smart, but forget everything

They don’t know: - your documents
- your code
- your data
- your knowledge base

RAG fixes this.

🔍 2. Retrieval finds the relevant information

Before the LLM answers, the system: - searches a vector database
- finds relevant chunks
- injects them into the prompt

This gives the LLM facts.

🧩 3. Augmentation = Adding context

RAG expands the model’s world.

Instead of:

“Write a summary.”

You give:

“Write a summary using these documents.”

Now the model stops hallucinating.

📚 4. RAG powers:

• document chat
• FAQ bots
• policy compliance systems
• knowledge assistants
• internal team tools

Even GhostFrog could eventually use RAG for classified product notes.

🚀 5. Why RAG matters

A chatbot is dumb.
A chatbot with your documents is useful.