RAG Knowledge Builder

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

Navinspire-ia Updated

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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.

Navinspire-ia/navin/tree/main/navin/skills/rag-knowledge-builder commit 2057a33bf1

Frequently asked questions

npx skillmds@latest add navinspire-ia/rag-knowledge-builder