RAG (Retrieval-Augmented Generation) usually requires setting up vector databases, embedding models, and chunking pipelines. I built a Claude Code skill that reduces this to a conversation: Install the skill Say “Create a knowledge base and upload my files” Ask questions and get answers grounded in your documents It uses the Denser Retriever API under the hood, which handles document parsing, semantic indexing, and neural reranking. The skill wraps all 13 API endpoints so Claude Code can construct the right curl commands from your natural language requests. Practical for teams that need document search (HR policies, legal contracts, research papers, support docs) without a dedicated engineering effort. Tutorial: https://retriever.denser.ai/blog/build-rag-knowledge-base-claude-code submitted by /u/True-Snow-1283
Originally posted by u/True-Snow-1283 on r/ArtificialInteligence