Skip to main content
AIDevOps
  • Learn
  • Learning Paths
  • Practice
  • Open Source
  • Books
  • Engineering

    AI DevOpsThe full map of building and operating AI servicesLLMOpsLLM deployment Β· evaluation Β· observabilityHands-on ProjectsBuild AI Agent projects

    Knowledge

    DocsTechnical documentationBlogEngineering articlesPloggerDevelopment log feed

    Validate

    Certification3-level skills certification Β· coming soon
AI Models
LlamaTutorial|MistralTutorial|GemmaTutorial|DeepSeekTutorial|QwenTutorial
🧠 AI Core
AI Intro & RoadmapML FundamentalsLLM Fundamentals|Python AIC++|PyTorchTensorFlowJAX
πŸ€– AI Applied Development
Applied AI Intro & RoadmapHugging FaceLangChainLlamaIndexLLMOps|LangGraphMCPMulti-AgentAgent Evaluation
🧠 AI Agent Development
Finance AI AgentLLM API ServerStock Investing AgentAIOps AI AgentEducation AI AgentCoding AI Agent
🌱 Spring Cloud
Spring Intro & RoadmapSpring Cloud GatewaySpring BootJava|Spring AISpring SecuritySpring BatchSpring JPA
🐳 DevOps
DevOps Intro & RoadmapLinuxDockerCI/CD|Kubernetes BasicsK8s AdvancedPrometheusGrafana
🧱 Infrastructure
Infrastructure Intro & RoadmapNginxRedis
☁️ Cloud
Cloud Intro & RoadmapAWSGCPAzureNCPCloudflare
🎨 Frontend
Frontend Intro & RoadmapJavaScriptTypeScript|ReactNext.js|VueNuxt|Electron
πŸ“± Mobile
Mobile Intro & RoadmapKotlinAndroidFlutter
βš™οΈ Backend
Backend Intro & RoadmapPython BasicsFastAPIDjangoFlask|CGoGinNode.js
πŸ’Ύ Database
DB Intro & RoadmapCore SQLOracleMySQLPostgreSQL|MongoDBVector DB
πŸ§ͺ Testing
k6JMeternGrinder
AIDevOps

Engineering AI. From Code to Production.
An engineering learning platform for building and operating AI and AI Agents

Learn

  • All Guides
  • Learning Paths
  • AI Tutorial
  • Practice
  • Books

Resources

  • AI DevOps
  • LLMOps
  • Hands-on Projects
  • Docs
  • Blog
  • Plogger
  • Open Source
  • Certification (coming soon)

Start Here

  • AI Core Roadmap
  • AI Applied Development Roadmap
  • Spring Cloud Roadmap
  • DevOps Roadmap
  • Infrastructure Roadmap

Β 

  • Cloud Roadmap
  • Frontend Roadmap
  • Mobile Roadmap
  • Backend Roadmap
  • Database Roadmap
Β© 2026 AIDevOps. All rights reserved.
Terms of ServicePrivacy PolicySitemaptestforge.kr
  1. Home
  2. AI Tutorial
  3. The Guild Library

Quest 08 Β· Developers

The Guild Library

Visitors

RAG: making an AI find documents and answer from them

Time
7 min
Max XP
100
Skill earned
RAG: answering from documents
1 / 5
0 XP

The librarian's request

Client

I am the guild librarian. Members ask me about the rules every day, but the rulebook is 300 pages, so even I take ages to find things.

Client

It would be wonderful if Ari could read the rulebook and answer for me. The rules change every few months.

Ari

I have never learned the guild rulebook. As in request 3, you would have to hand me the material. But if I am handed 300 pages every time…

Summary

What this request teaches

How do you get an AI to answer from a 300-page rulebook? Through the guild library's request, learn the structure of RAG, which cuts documents into pieces and finds the right ones, and where to look first when an answer is wrong.

Show the key points (best read after finishing the request)
  • RAG stores documents cut into pieces, finds only the pieces related to a question, and hands them to the AI with the question.
  • Pieces are turned into embeddings (lists of numbers that stand for meaning) and stored in a vector database, and pieces close in meaning are found.
  • For facts that change often or answers that must cite a source, RAG fits better than fine-tuning.
  • Instructing "Answer only from these documents and cite the source" lets a person check the original.
  • When an answer is wrong, look at finding and answering separately. You can only find the cause if you record the pieces found for each question.
  • When you change documents, delete the old pieces or tag them with information such as an effective date.

Try it yourself

Pick a document you have (a manual, rules, meeting notes), remove personal details, and paste it into an AI with the request below. It is a small RAG done by hand.

Answer my question using only the document below.
- Quote the part your answer is based on, word for word.
- If the document has no basis for an answer, reply only "Not in the document".

Question: [question]

Document:
[paste the document here]

Learn more

  • AI App Intro β€” the overall structure of RAG β†’
  • Vector DB β€” embeddings and similarity search β†’
  • LlamaIndex β€” building a document search pipeline β†’
  • Back to the request board β†’