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Lesson

Ingestion, cleaning, and chunking

Chunking is not about cutting text every thousand characters. It is about preserving meaning, headings, tables, dates, permissions, and source so retrieval can find useful context.

  • Prepare documents before converting them into vectors.
  • Choose a chunking strategy based on document type.
  • Store metadata for permissions, citations, and auditing.

Minimum metadata

Terminal
{
  "document_id": "contrato-2026-001",
  "title": "Contrato proveedor",
  "source": "drive/legal/contrato.pdf",
  "page": 12,
  "section": "penalizaciones",
  "owner": "legal",
  "visibility": "internal",
  "updated_at": "2026-07-02"
}

Chunking strategies

  • By headings: manuals, policies, technical documentation.
  • By page: contracts, case files, PDFs with page-level citations.
  • By table: invoices, catalogs, inventories.
  • With overlap: narrative text where an idea spans paragraphs.
Useful RAG: much more than PDF chatAdvanced Chunking for Private RAG
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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.

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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.

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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.

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

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