knowledge-base-rag

Retrieval-augmented generation prevents hallucinations by grounding LLM responses in your private documents. It constrains generation to retrieved context with explicit citation output, ensuring answers are sourced from up-to-date, organization-specific knowledge bases.

The best way to chunk documents for semantic vector search is using recursive structure-aware chunking. This method respects headings, code blocks, and tables while applying overlap windows to preserve context for precise retrieval and re-ranking.

To build an end-to-end RAG pipeline, you ingest private documents, extract text and metadata, apply semantic chunking, embed the content, and index it in a vector store. This enables semantic top-k retrieval and grounded question answering.

Yes, retrieval-augmented generation supports citations for internal product Q&A. It performs semantic top-k retrieval with re-ranking to improve precision, then constrains the generated answer to the retrieved context and provides explicit citation output.

Yes, you can use semantic chunking with vector store indexes. The pipeline extracts text and metadata, applies recursive splitting, and preserves metadata during indexing to maintain context for downstream semantic search and question answering.