RAG Expert

To build a RAG system that grounds LLM responses, index your enterprise knowledge base, select an embedding model, and connect a vector store to an LLM. This creates a pipeline for retrieving relevant documents and generating answers with citations from internal sources.

Chunking strategies for retrieval-augmented generation include fixed-size, sentence-based, semantic, and document-structure chunking. Document-structure chunking preserves hierarchical context, while semantic chunking groups related content to improve retrieval accuracy and grounding.

Yes, you can use cross-encoder reranking to improve vector search accuracy in a knowledge base assistant. Reranking refines initial embedding-based search results by scoring document-query pairs more deeply, ensuring the most relevant chunks are passed to the LLM.

The best way to answer questions with citations from internal policy PDFs and runbooks is deploying a grounded RAG architecture. It combines offline indexing, embedding storage, and vector search to retrieve exact passages, allowing the LLM to generate responses with verifiable source citations.

A production-grade RAG pipeline requires a modular setup including an embedding model for vector generation and a vector store for storage. The architecture is reproducible and modular, allowing you to choose compatible components that support query transformation and reranking.

You should not use retrieval-augmented generation for a domain-specific chatbot when your workflow lacks document-heavy knowledge bases or when real-time data is needed instead of static indexed documents. RAG is designed for reproducible answers grounded in pre-indexed enterprise content.