knowledge-base-rag

Ingest private documents into a vector index and generate cited answers.

31|8|Updated Apr 10, 2026

Search Tags:#embeddings #rag #knowledge base #citations #vector search #document qa #semantic chunking

Ready to run

npx skills add https://github.com/itallstartedwithaidea/agent-skills --skill knowledge-base-rag

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: knowledge-base-rag
Source: https://github.com/itallstartedwithaidea/agent-skills/tree/main/skills/productivity/knowledge-base-rag
Command: npx skills add https://github.com/itallstartedwithaidea/agent-skills --skill knowledge-base-rag

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) components.

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.

Frequently Asked Questions about knowledge-base-rag

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I prevent LLM hallucinations when answering questions from private documents?▼

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.

What is the best way to chunk documents for semantic vector search?▼

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.

How do I build an end-to-end RAG pipeline for a company wiki?▼

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.

Does retrieval-augmented generation support citations for internal product Q&A?▼

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.

Can I use semantic chunking with existing vector store indexes?▼

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.