The 30-Day Roadmap I’d Follow to Learn LLMs and AI Agents

If I were starting to learn LLMs and AI Agents today, I wouldn’t jump into building a complex autonomous agent right away. Instead, I’d use the first few weeks to learn how LLMs work, how to give them clear instructions, how to connect them to outside information, and how to make them use tools. So, in this article, I’ll share the 30-day roadmap I’d follow to learn LLMs and AI Agents, step by step, if I were starting over.

30-Day Roadmap to Learn LLMs and AI Agents

Week 1: Understand How LLMs Work

During the first week, I’d focus on building a solid foundation.

I wouldn’t spend a lot of time on every detail of transformer architecture. Instead, I’d focus on the concepts that matter most for building LLM applications.

Days 1–2: Learn LLM Fundamentals

I’d start with:

1. What are Large Language Models?
2. Tokens and tokenization
3. Embeddings
4. Transformer architecture
5. Attention mechanism
6. Context windows
7. Pretraining vs. fine-tuning
8. Inference
9. Temperature and other generation parameters

One thing I learned early on is that you don’t have to be a researcher to build useful LLM applications. But it’s important to know enough about the model to understand why it works the way it does.

Days 3–4: Work With an LLM API

Next, I’d start using a model by building small Python programs that:

→ Send prompts to an LLM
→ Generate text
→ Control temperature
→ Handle conversation history
→ Stream responses
→ Process structured outputs

Find an example to work with the Gemini API here.

The goal here isn’t to create something impressive. It’s to get comfortable working with an LLM through code.

Days 5–7: Learn Prompt Engineering

Then I’d spend a few days trying out different prompts and practicing:

1. Zero-shot prompting
2. Few-shot prompting
3. Role-based instructions
4. Structured prompts
5. Chain-of-thought-style task decomposition
6. Output constraints
7. Prompt templates

Recommended Projects + Resources:

1. Hands-on GenAI, LLMs and AI Agents
2. LLM Projects for Absolute Beginners

Week 2: Learn RAG, Embeddings and Tool Calling

At this point, I’d move from basic LLM apps to ones that can use outside information.

Days 8–10: Learn Embeddings and Vector Search

I’d learn how to turn text into embeddings and compare them based on meaning. I’d try out tools like:

1. Sentence Transformers
2. FAISS
3. Chroma
4. Qdrant

Then I’d build a tiny semantic search engine. This would help me understand what happens before an LLM generates an answer.

Days 11–13: Build a RAG Application

Next, I’d build my first proper Retrieval-Augmented Generation (RAG) application.

I’d begin with simple text or PDF files, then build a chatbot that can answer questions using those documents. I highly recommend this project because it teaches several key AI engineering concepts at the same time.

I’d also try changing chunk size, overlap, how many results to retrieve, and how I design prompts.

Day 14: Learn Tool Calling

After learning RAG, I’d find out how LLMs can work with external tools. I’d build simple tools like:

1. Calculator
2. Weather lookup
3. Web search
4. Database query
5. Python function

The key concept I’d focus on is that the LLM doesn’t magically execute the tool. It decides which tool to call and what arguments to provide, while my application executes the actual function.

This difference is very important when you start building agents.

Recommended Projects + Resources:

1. Hands-on GenAI, LLMs and AI Agents
2. Build an AI Code Review Bot for GitHub
3. The Multilingual RAG Pipeline

Week 3: Understand AI Agents

Now I’d shift from learning single skills to building agent workflows.

Days 15–17: Learn What Makes an AI Agent

I’d study the core components of an AI agent:

1. Goal
2. Instructions
3. Reasoning or planning
4. Tools
5. Memory
6. State
7. Feedback loops
8. Execution

Then I’d build a simple agent that can decide which tool to use based on the user’s request.

A common mistake is thinking that adding more tools always makes a better agent. In reality, a smaller and more predictable workflow is usually much easier to debug.

Days 18–20: Add Memory

Next, I’d learn how memory works. I’d begin with short-term memory so the agent can remember earlier parts of the conversation.

Then I’d explore longer-term memory using a database or vector store. I’d build a personal assistant that remembers things such as:

1. User preferences
2. Previous conversations
3. Important facts
4. Tasks and goals

This would help me see the difference between context, state, and long-term memory.

Day 21: Build a Complete AI Agent

At this stage, I’d put everything together and build an agent that can:

1. Understanding a user request
2. Deciding what information it needs
3. Calling tools
4. Retrieving relevant knowledge
5. Maintaining conversation state
6. Producing a final response

This would be my first complete agentic AI project from start to finish. You can find an example here.

Recommended Projects + Resources:

1. Hands-on GenAI, LLMs and AI Agents
2. 10 Real-World AI Agent Projects

Week 4: Master LangGraph and Build Real Applications

In the last week, I’d focus on building more structured, production-ready systems.

Days 22–24: Master LangGraph

I’d start learning LangGraph since it gives you a clear way to build stateful, multi-step agent workflows. I’d focus on:

1. Nodes
2. Edges
3. State
4. Conditional routing
5. Loops
6. Tool execution
7. Human-in-the-loop workflows

Instead of only following tutorials, I’d rebuild my earlier agents with LangGraph. Comparing the two would help me see why frameworks matter, not just how to use them.

Days 25–27: Build an Agentic RAG System

Next, I’d combine RAG and agents. I’d build an Agentic RAG application where the system can decide when it needs to retrieve information and when it can answer directly.

This is where everything from the past three weeks finally comes together.

Days 28–30: Build and Deploy a Real AI Application

For the last three days, I wouldn’t pick up another framework. Instead, I’d build something real that I could add to my portfolio.

Some ideas would be:

1. Build a Self-Correcting Code Assistant
2. Build an AI Agent for End-to-End App Development
3. Agentic AI Pipeline to Automate EDA
4. Vision AI App

I’d use something simple like Streamlit to build the interface, make the app available through an API if needed, and deploy it on a cloud platform or locally.

Most importantly, I’d make sure to document the project well. I’d cover the architecture, technologies used, challenges, limitations, and what I learned. This turns a tutorial project into something I can talk about in an interview.

Recommended Courses for Learning LLMs and AI Agents

If you want a more structured way to learn alongside this 30-day roadmap, I highly recommend these two programs:

1. Generative AI with Large Language Models: This course is perfect for the first two weeks of the roadmap. It gives you a deep, practical understanding of how Large Language Models and transformers actually work under the hood, helping you master prompt engineering and model interactions.
2. IBM RAG and Agentic AI Professional Certificate: This program aligns perfectly with the final two weeks of your journey. It provides hands-on experience with advanced RAG pipelines, LangGraph, and building the exact kind of autonomous agent architectures I recommend for your portfolio.

Both programs focus on practical, hands-on software engineering skills that help you turn theory into real AI applications.

What I’d Focus on After 30 Days

After these 30 days, I wouldn’t call myself an expert in LLMs or AI Agents, and that’s totally fine.

My goal would be to reach a point where I can understand an LLM app, build one from scratch, connect it to outside knowledge and tools, and figure out how an agentic system works.

After that, I’d dive deeper into things like fine-tuning, evaluation, LLMOps, observability, multimodal AI, advanced RAG, multi-agent systems, and deploying to production. That’s exactly how I’d tackle these 30 days.

I hope you enjoyed this article on the 30-day roadmap I’d use to learn LLMs and AI Agents, step by step.

For more tips on AI and machine learning, feel free to follow me on Instagram. My book, Hands-On GenAI, LLMs & AI Agents, can also help you grow your AI career.