Local second brain with Obsidian and RAG

Obsidian works very well as a knowledge base because your notes are Markdown. The challenge isn't reading files: it's cleaning, tagging, filtering, and citing without turning your second brain into vector noise.

Prepare Markdown notes for RAG without losing context.
Use frontmatter and folders as metadata.
Avoid mixing private notes, drafts, and reliable knowledge.

Terminal

---
title: "Returns Policy"
type: "source"
status: "active"
tenant_id: "personal"
tags: ["support", "legal"]
---

# Returns Policy

Clear, up-to-date text with a date.
Related links: [[Warranties]], [[Customer Support]]

What to exclude

Personal journals if they aren't needed.
Unreviewed drafts.
Notes with secrets, keys, or third-party data.
Empty templates and duplicate files.

Terminal

payload:
  source: "obsidian/soporte/devoluciones.md"
  title: "Returns Policy"
  status: "active"
  tags: ["support", "legal"]
  backlinks: ["Warranties", "Customer Support"]
  updated_at: "2026-07-05"

Official sources

Obsidian basic formatting syntax
Obsidian Flavored Markdown
Qdrant documentation

Qdrant Multi-User and Permissions Prompt injection in RAG

Complete Aulafy mapSee how this lesson fits without leaving your path.

Complete Aulafy map

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.

1Understand
2Apply or build
3Operate with confidence

Choose an application

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

AI for small businesses and freelancersUse AI where it improves real work.Beginner to intermediate · 20 lessonsView course Generative AI: image, voice and videoCreate production-ready media with AI pipelines.Beginner to intermediate · 7 lessonsView course Games, 3D and simulations with AIPrototype interactive worlds with AI-assisted tools.Beginner to intermediate · 6 lessonsView course

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

Build with code

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

Aulafy foundationsPython, Git, terminal, and Docker for learning AI smoothly.Beginner · 6 lessonsView course Codex for programmersBuild, test, and ship changes with OpenAI Codex.Beginner to advanced · 10 lessonsView course Claude Code, from zero to proBuild real software by talking to AI in your terminal.Beginner to advanced · 24 lessonsView course Claude Code + Local AIPrivate AI tools that run on your machine.Intermediate · 34 lessonsView course

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

Take systems to production

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

Advanced and secure RAGYou are hereBuild retrieval systems that answer better and leak less.Intermediate · 14 lessonsView course Agents and automationDesign agents that can work, recover and stay under control.Intermediate to advanced · 20 lessonsView course Production agents with LangGraph and n8nTake agent prototypes into reliable operations.Intermediate to advanced · 9 lessonsView course Self-hosted AI automation for small businessesRun AI workflows on your own infrastructure.Intermediate · 10 lessonsView course AI Router and content systemRoute local and frontier models with quality, cost, and privacy controls.Intermediate to advanced · 6 lessonsView course AI security and evaluationMeasure, test and harden AI systems before trusting them.Intermediate · 7 lessonsView course Local MLOps and model deploymentServe, monitor and operate models from your own stack.Intermediate to advanced · 10 lessonsView course Fine-tuning and post-training for LLMsTrain useful adapters without losing control.Intermediate to advanced · 8 lessonsView course

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

View full catalogue