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Advanced Chunking for Private RAG

Many RAG systems fail before they reach the model: they cut paragraphs in half, lose headings, mix topics, and don't store metadata. Advanced chunking means preserving meaning, not just counting tokens.

  • Avoid fixed-size chunks that split ideas.
  • Store headings, page, document, date, and permissions.
  • Evaluate retrieval with real questions.
Terminal
chunk:
  text: "The customer can return the product within 14 days..."
  metadata:
    document_id: "politica-devoluciones"
    heading_path: ["Customer support", "Returns"]
    page: 3
    language: "es"
    updated_at: "2026-07-05"
    tenant_id: "acme"
    source_url: "docs/devoluciones.pdf"

Practical rules

  • Split by sections, paragraphs, and sentences before fixed size.
  • Use overlap when an explanation continues across chunks.
  • Don't mix tables, legal notes, and narrative in the same fragment.
  • Store metadata to filter before searching.
  • Evaluate with questions users would actually ask.
Terminal
eval_recuperacion:
  pregunta: "How many days do I have to return a product?"
  debe_recuperar:
    - document_id: "politica-devoluciones"
      heading: "Returns"
  no_debe_recuperar:
    - "garantias"
    - "envios"

Official sources

  • Qdrant documentation
  • Qdrant filtering
  • Chunkr GitHub
  • Chunkr docs
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How all courses connect

This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.

  1. 1Understand
  2. 2Apply or build
  3. 3Operate with confidence
01

Choose an application

Turn the foundation into a visible outcome: a website, a business improvement, media, or an interactive experience.

Continue into the technical branch when you need to maintain code, data, or infrastructure.

02

Build with code

Prepare your environment, work with coding agents, and run models while keeping control of your projects.

This branch prepares you to design and operate reliable AI systems.

03

Take systems to production

Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.

You do not need every course: choose the component your system needs and return as it grows.

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