# RAG Knowledge Builder

> Build and evaluate RAG corpora - ingest, chunk, embed, index, and spot-check retrieval quality. Use when creating knowledge bases for agents.

- Skill: `navinspire-ia/rag-knowledge-builder` (Agent Skill)
- Install (CLI): `npx skillmds@latest add navinspire-ia/rag-knowledge-builder`
- Raw SKILL.md: https://api.skillmd.com/api/skills/navinspire-ia/rag-knowledge-builder/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: Navinspire-ia (https://skillmd.com/u/navinspire-ia)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/navinspire-ia/rag-knowledge-builder

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# RAG Knowledge Builder

## Overview

Garbage in, garbage out. Clean sources beat clever chunkers.

## Workflow

1. Define the question types the RAG must answer.
2. Ingest sources (docs, `web-extractor` output, PDFs).
3. Chunk with structure awareness (headings > fixed blind windows).
4. Embed/index with the project’s vector store (note model + dims).
5. Evaluate with 10-20 gold questions; measure hit rate / faithfulness.
6. Fix gaps (missing docs, bad chunking) before tuning prompts.

## Rules

- Track provenance (source URL/path) on every chunk.
- Exclude secrets and credentials from the corpus.