AI Learning Hub

The Complete AI Roadmap
for Developers

Learn LLMs, Prompt Engineering, RAG Systems, AI Agents, and Fine-Tuning with a structured step-by-step learning path.

7 phases · ~14 months · 50+ resources · real projects

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Not just links

Topics, projects & milestones — not a bookmark list.

Built for developers

No math papers. Ship AI products from day one.

Track progress

Check off topics. Saved in your browser forever.

Roadmap Preview

Where are you starting?

How to use this roadmap
Check off topics

Click any topic in the Learn tab to track your progress. Saved in your browser.

Explore tabs per phase

Each phase has Learn, Resources, and Project tabs — the project tells you what to build.

More tools above

Use the tool links above to check readiness, run an assessment, or explore guides — all free.

01
Phase 1 – AI FoundationsUnderstand the landscape
4–6 weeks5 topics5 resources

Understand how AI/ML works conceptually. No heavy math — just intuition and vocabulary.

  • Click any topic to check it off and track your progress
  • What is AI, ML, Deep Learning, and GenAI — and how they relate
  • Neural networks: inputs, weights, layers, outputs
  • How models learn (gradient descent, loss functions)
  • What LLMs are and how they generate text
  • Key terms: tokens, embeddings, parameters, inference
02
Phase 2 – LLM Setup & ConfigurationGet your environment ready
2–3 weeks8 topics4 resources

You will be able to:

Run LLMs locally with Ollama

Call OpenAI, Anthropic, and Gemini APIs from code

Build: Run Llama 3 locally via Ollama. Call it from a Python script. Compare output vs Claude/GPT API.

03
Phase 3 – Prompt Engineering & LLM APIsStart building immediately
3–4 weeks5 topics4 resources

You will be able to:

Ship your first AI-powered app

Write zero-shot, few-shot, and chain-of-thought prompts

Build: Build a CLI or web tool powered by an LLM API — a code reviewer, doc summarizer, or Q&A bot.

04
Phase 4 – RAG & Working with Your Own DataMake AI know your domain
4–5 weeks5 topics3 resources

You will be able to:

Build document Q&A chatbots over any data

Integrate vector databases (Chroma, Pinecone)

Build: Build a chatbot that answers questions from your own PDF documents or a knowledge base you care about.

05
Phase 5 – Agentic AIAI that thinks and acts
4–5 weeks8 topics4 resources

You will be able to:

Build agents that plan and execute tasks autonomously

Use tool-calling and function-calling APIs

Build: Build an agent that can search the web, read a URL, and write a summary report — a mini Perplexity.

06
Phase 6 – Building & Training LLMsGo deep under the hood
6–8 weeks7 topics5 resources

You will be able to:

Fine-tune an LLM on your own data with QLoRA

Know when to prompt vs RAG vs fine-tune

Build: Fine-tune Llama 3 8B on a custom dataset using QLoRA + Unsloth on free Google Colab GPU.

07
Phase 7 – Build & Ship Real ProjectsWhere knowledge becomes mastery
Ongoing5 topics4 resources

You will be able to:

Deploy AI apps to production

Build a public portfolio of real AI projects

Build: Pick one meaningful personal project — a research assistant, coding tool, or AI for your hobby — and launch it publicly.

After completing this roadmap, you can:

Build and deploy production AI applications
Design RAG systems over any data source
Create autonomous AI agents with tool use
Fine-tune open-source LLMs on custom data
Integrate AI into any web or API stack
Speak about AI architecture with real depth

Principles for the Journey

Build early, build often: Don't wait until you feel 'ready'. Ship something after Phase 3.
Concepts > memorization: Understand the why. Tools change fast, intuition doesn't.
Your dev skills are a superpower: You can go from idea to working AI app faster than most beginners.
Prompt → RAG → Fine-tune: Always try the simplest approach first before going deeper.
Projects are your portfolio: Even personal projects signal domain expertise better than certificates.

Help make this better

This roadmap is community-driven. If you spot something outdated, missing, or wrong — please say so.

View all discussions on GitHub

Click each phase to expand · Use tabs to navigate sections