43 Vector Databases Rag Langchain Panaversity Agentfactory

---
sidebar_position: 0
title: "Build Your RAG Skill"
description: "Create your RAG skill in one prompt, then learn to improve it throughout the chapter"
chapter: 43
lesson: 0
duration_minutes: 20

# HIDDEN SKILLS METADATA
skills:
  - name: "Skill-First Learning"
    proficiency_level: "B1"
    category: "Applied"
    bloom_level: "Apply"
    digcomp_area: "3. Digital Content Creation"
    measurable_at_this_level: "Student creates a working RAG skill using natural language"

learning_objectives:
  - objective: "Build a RAG skill using natural conversation with Claude"
    proficiency_level: "B1"
    bloom_level: "Apply"
    assessment_method: "Student has a working rag-deployment skill in .claude/skills/"

cognitive_load:
  new_concepts: 1
  assessment: "Single concept: use Claude to build a skill from official docs"

differentiation:
  extension_for_advanced: "Add hybrid search and re-ranking patterns during creation"
  remedial_for_struggling: "Follow exact prompt provided"
---

# Build Your RAG Skill

Before learning RAG (Retrieval-Augmented Generation)—the architecture that gives AI agents access to your private data—you will **own** a RAG skill.

---

## Step 1: Get the Skills Lab

1. Go to [github.com/panaversity/claude-code-skills-lab](https://github.com/panaversity/claude-code-skills-lab)
2. Click the green **Code** button
3. Select **Download ZIP**
4. Extract the ZIP file
5. Open the extracted folder in your terminal

```bash
cd claude-code-skills-lab
claude
```

---

## Step 2: Create Your Skill

Copy and paste this prompt:

```
Using your skill creator skill create a new skill for RAG (Retrieval-Augmented
Generation) systems. I will use it to build production RAG pipelines with
LangChain and Qdrant vector database - from simple semantic search to advanced
patterns like HyDE, CRAG, and Agentic RAG. Use context7 skill to study official
LangChain and Qdrant documentation and then build it so no self assumed knowledge.
```

Claude will:
1. Fetch official LangChain and Qdrant documentation via Context7
2. Ask you clarifying questions (chunking strategies, embedding models, retrieval patterns)
3. Create the complete skill with ingestion pipelines, retrieval patterns, and evaluation templates

Your skill appears at `.claude/skills/rag-deployment/`.

---

## Done

You now own a RAG skill built from official documentation. The rest of this chapter teaches you what it knows—and how to make it better.

**Next: Lesson 1 — Understanding RAG Architecture**