RAG

RAG means Retrieval-Augmented Generation. It lets an LLM answer using trusted documents instead of only relying on what the model already knows.

Simple idea: search first, answer second.

Why It Matters

RAG is useful when the answer must be grounded in company knowledge:

• Policies.
• Product docs.
• Support history.
• Legal documents.
• Engineering decisions.
• Customer notes.
• Research reports.

How It Works

1. Collect documents.
2. Split them into small chunks.
3. Convert chunks into Embeddings.
4. Store embeddings in a Vector Database.
5. Search for relevant chunks when the user asks a question.
6. Send those chunks to the LLM.
7. Generate an answer with citations or references.

Simple Architecture

User question
  -> retrieve relevant docs
  -> send docs + question to LLM
  -> answer with grounded context

Good Use Cases

Use case	Example
Internal knowledge copilot	Ask questions over company docs
Compliance assistant	Answer using GDPR/DPDP/CCPA notes
Support copilot	Suggest answers from previous tickets
Engineering assistant	Search architecture decisions
Sales assistant	Draft proposals from approved material

Risks

• Bad documents produce bad answers.
• Outdated documents create wrong answers.
• Poor chunking hides context.
• Retrieval may miss the right source.
• Sensitive documents need access control.

Production Checklist

• Keep source documents clean.
• Track document freshness.
• Add access permissions.
• Show sources in the answer.
• Monitor failed searches.
• Let users report wrong answers.

CookieYes Ideas

• RAG over privacy laws and CookieYes documentation.
• RAG over consent implementation guides.
• RAG over customer support tickets.
• RAG over AI governance policies.

Mozilor Ideas

• RAG over accessibility standards and WebYes docs.
• RAG over WooCommerce plugin documentation.
• RAG over agent action logs and playbooks.
• RAG over internal product research.

Related

• LLM
• Embeddings
• Vector Database
• Agents
• MCP