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
Developer Guides
Deep-dives on ML, LLMs, RAG, prompt engineering, and AI agents.
AI Projects
Hands-on builds from beginner chatbots to multi-agent systems.
Career Paths
Role-based paths for AI Engineer, ML Engineer, LLM Engineer.
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?
Click any topic in the Learn tab to track your progress. Saved in your browser.
Each phase has Learn, Resources, and Project tabs — the project tells you what to build.
Use the tool links above to check readiness, run an assessment, or explore guides — all free.
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
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.
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.
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.
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.
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.
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:
Essential Tools You'll Use
Principles for the Journey
Help make this better
This roadmap is community-driven. If you spot something outdated, missing, or wrong — please say so.
Click each phase to expand · Use tabs to navigate sections