The power of RAG Stack?
Data Preparation Pipeline: This prepares external knowledge and stores it in a way that LLMs can retrieve it. Retrieval + Generation Process: This is the actual workflow when a user sends a query. Data Preparation Pipeline RAG Workflow Why RAG is Powerful? Overcomes LLM memory limitations: Instead of relying only on training data, it pulls from external knowledge sources. Keeps responses up-to-date: Can access fresh data without retraining the model. Domain-specific expertise: Can ground answers in private enterprise data (legal docs, research papers, medical reports). Improves factual accuracy: Reduces hallucinations by constraining answers to real retrieved content. Summary of Workflow Prepare Data → Collect, extract, chunk, embed, and store in vector DB. At Query Time → Convert user query into embedding → search DB → retrieve relevant chunks. Generation → Provide both query + retrieved data to LLM → generate grounded response. This architecture is the ...