knowledge-base-rag

What problem does it solve?

Knowledge-base RAG prevents hallucinations by grounding LLM responses in up-to-date, organization-specific documents instead of relying on training data alone.

Core Features & Use Cases

• End-to-end RAG pipeline: ingest documents, extract text and metadata, chunk intelligently, embed, index in a vector store, retrieve relevant passages, and generate grounded answers with citations.
• Production-ready chunking: uses semantic chunking and recursive splitting that respects headings, code blocks, tables, and overlap windows to preserve context for retrieval.
• Retrieval quality improvements: performs semantic top-k search and adds re-ranking to improve precision before generation.
• Use cases: internal product Q&A, customer support over private docs, searchable company wikis, and document-embedded semantic search for large knowledge collections.

Quick Start

Use the knowledge-base-rag skill to ingest your documentation files and ask a question so it returns an answer with cited sources from your knowledge base.