Retrieval-Augmented Generation (RAG)

About Retrieval-Augmented Generation (RAG)

• It is a technique that makes AI more accurate by allowing it to search your organization’s own documents before answering a question.
• Features: 
  • Instead of relying only on what the AI learned during training, RAG:
  • Searches relevant documents from a private knowledge base.
  • Uses those documents to generate an answer.
  • Shows the source of the information, making answers more trustworthy.
• How Does it Work: It works in two phases:
  • Phase 1: Build the Knowledge Base (Done Once)
    • Company documents (reports, interview transcripts, policy manuals, legal documents, support tickets, etc.) are collected.
    • These documents are divided into smaller meaningful sections called chunks.
    • Each chunk is converted into a numerical representation called an embedding vector, which captures its meaning rather than just the words used.
    • All these vectors are stored in a vector database.
  • Phase 2: Answer User Questions (Every Time):  
    • When a user asks a question, the question is also converted into an embedding vector.
    • The system searches the vector database for the most similar information based on meaning, not exact keywords.
    • The relevant document sections are sent to the AI.
    • The AI generates an answer using only those retrieved documents.
    • It also cites the source documents and may provide a confidence score.
• Application: