knowledge-base-rag
Knowledge Base RAG
Part of Agent Skills™ by googleadsagent.ai™
Description
Knowledge Base RAG implements the complete Retrieval-Augmented Generation pipeline: document ingestion, intelligent chunking, embedding generation, vector store indexing, semantic retrieval, and grounded response generation. The agent builds RAG systems that answer questions from private knowledge bases with cited sources and reduced hallucination.
RAG solves the fundamental limitation of large language models: they cannot access information created after their training cutoff or proprietary information they were never trained on. By retrieving relevant documents from a vector store and injecting them into the prompt context, RAG grounds the model's responses in factual, up-to-date, organization-specific knowledge.
The quality of a RAG system depends on chunking strategy more than model choice. This skill encodes production-tested chunking approaches: semantic chunking that preserves paragraph coherence, recursive splitting that respects document structure (headings, code blocks, tables), and overlap windows that maintain context across chunk boundaries. Each strategy is matched to the document type for optimal retrieval quality.