How RAG Solves LLM Hallucinations and Knowledge Boundaries
Foundation LLMs store knowledge implicitly in model weights, making them prone to hallucinations and unable to access private, real-time company documents. RAG decouples knowledge storage from reasoning logic.
When a user asks a question, the RAG engine converts the query into a vector, searches an enterprise vector database (such as Pinecone or Qdrant), retrieves top-k relevant document chunks, and passes them as explicit context to the model. The model synthesizes an answer grounded strictly in the retrieved evidence.
Components of Enterprise RAG Pipelines
1. Document Ingestion & Chunking: Parsing PDFs, HTML, and Markdown into optimal semantic text chunks.
2. Vector Indexing: Generating dense embeddings and indexing them for fast similarity search.
3. Hybrid Search & Reranking: Combining vector search with BM25 keyword matching and cross-encoder reranking.
4. Grounded Generation & Citation: Generating responses with inline source citations pointing to exact source documents.
RAG Evaluation: Measuring Faithfulness and Recall
We evaluate RAG pipelines using frameworks like RAGAS, measuring faithfulness (zero ungrounded claims), context precision (signal-to-noise ratio), and answer relevance.