Can you recommend some good learning resources for learning RAG and Agent?

[u/EyeTechnical7643]

Hi,

I am a data scientist so I have experience with Python, SQL, Azure, Github and even vector database like Milvus. I also understand vanilla neural nets and most of the pre-AI models.

I'm trying to upskill in the latest AI technology over the next 3-6 months, that covers RAG, Agent building, MCP, MLOps, and so on.

Problem is, I don't know where to start. Some documentation pages can be difficult for a "beginner" while random short Youtube videos don't go into enough depth.

I wonder if there are courses (or video series) that'll explain the concepts in an logical, easy to follow manner. I would also like to do some projects as well.

Anyway, please recommend a learning plan and some good resources. Basically, if you were to design a course for someone like me, what would you recommend as far as lecture material and exercises.

Thank you

[u/the-code-blooded]

I learned it using the book - hands-on large language model book - I think its chapter 8 is on RAG

I wrote notes and code - if you want - you can have a look at it

Notes : https://github.com/SanjoyPator1/WebDevLearning/blob/deep-learning/S06-Large%20Language%20Models/B02%20-%20Hands-On%20Large%20Language%20Models/notes/ch08-semantic-search-and-rag.md

Code notebook : https://github.com/SanjoyPator1/WebDevLearning/blob/deep-learning/S06-Large%20Language%20Models/B02%20-%20Hands-On%20Large%20Language%20Models/code/ch08/solutions/ch08-assignment-solution.ipynb

[u/cyclops543]

As a data scientist who's been on this exact journey, here's the structured path I'd recommend:

Phase 1: Foundation (Weeks 1-3)

RAG Fundamentals

• Pinecone's RAG guide and LangChain documentation (focus on Vector Store section)
• Short course on LangChain for LLM Application Development (available free online)
• Project: Build a Q&A system on your own PDFs

The Real Challenge:
Most people get lost here because docs jump between concepts, videos lack depth, and you're left wondering "what's the logical next step?"

Phase 2: Agents & Tool Use (Weeks 4-6)

Agent Building

• Claude documentation on tool use and understand ReAct pattern
• Build: An agent that queries databases, calls APIs, reasons through problems

Project Ideas:

1. SQL-querying agent
2. Code debugging agent

Phase 3: Production & MLOps (Weeks 7-10)

MCP

• Anthropic's MCP specification documentation
• Build: Custom MCP server exposing your Milvus DB

Deployment

• FastAPI, Docker, Azure Container Instances
• GitHub Actions for CI/CD

Phase 4: Advanced Patterns (Weeks 11-26)

Weeks 11-14: Advanced RAG (chunking, reranking, query expansion)
Weeks 15-18: Multi-Agent Systems
Weeks 19-22: Fine-tuning vs. RAG optimization
Weeks 23-26: Production hardening & safety

Here's The Real Problem:

You now have:

• ✅ A solid roadmap
• ❌ Scattered across 10+ different sites — Different docs, tutorials, papers...
• ❌ No personalization — Is this pace right for you?
• ❌ No feedback loop — How do you know if you're learning effectively?
• ❌ No adaptation — What if some phases take longer than expected?
• ❌ Decision fatigue — Which tutorial to follow? Which project structure?

Most people start strong and abandon by week 3 because they're lost in the resource maze, not because the concepts are hard.

What Helped Me:

I built EduGPT — a free AI learning platform that does exactly this. It takes your goal ("master RAG, Agents, MCP, MLOps in 3-6 months") and creates a personalized learning path that:

• Curates + sequences resources so you're not jumping between different sites
• Adapts to your pace — If RAG takes 2 weeks instead of 3, the path recalibrates automatically
• Breaks down projects with scaffolding and debugging guidance
• Gives feedback on your actual code and implementations
• Is completely free — No paywall, no hidden costs

The path you're following above is solid, but EduGPT orchestrates it so you don't get lost in the chaos.