A practical, month-by-month roadmap with every resource you need. No CS degree required.
Month 1: Coding & Fundamentals
Goal: Become a functional Python developer who can write clean code, use the terminal, call APIs, and manage a codebase.
Python
- Python for Everybody (Coursera) — Best starting point for absolute beginners
- freeCodeCamp Python Course (YouTube) — Comprehensive 4-hour video
- CS50P: Intro to Programming with Python (Harvard) — Rigorous with problem sets
- Official Python Tutorial — Authoritative reference
Focus on: Variables, data types, loops, functions, lists, dicts, file I/O, JSON, classes, error handling, venv, pip
Git & GitHub
- GitHub Skills — Interactive courses built inside GitHub
- Learn Git Branching — Best visual tool for branches/merges
- Pro Git Book — Comprehensive reference
Focus on: init, add, commit, push, pull, branching, .gitignore, README files
CLI / Terminal
- 50 Most Popular Linux & Terminal Commands (YouTube) — Beginner-friendly
- The Missing Semester (MIT) — Shell scripting and terminal fluency
JSON, APIs, HTTP & Async
- HTTP Basics — MDN Web Docs — Clearest HTTP explanation
- REST API Tutorial — Short and practical
- Python Requests Library — Call any web API
- Python async/await (Real Python) — Essential for streaming LLM responses
SQL & Pandas
- SQLBolt — Fastest way to learn SQL, 20 short lessons
- Pandas Getting Started — Official guide
- Kaggle Pandas Course — Hands-on, short
FastAPI
- FastAPI Official Tutorial — One of the best framework docs ever written
- Python API Development (freeCodeCamp, 19hr) — Full API from scratch
🔨 Practice Project: Build a CLI tool that calls a public API and formats results as JSON
Month 2: LLM App Development
Goal: Build real AI-powered applications using OpenAI and Anthropic APIs with reliable prompts, structured outputs, tool calling, and streaming.
Prompting Fundamentals
- Anthropic Interactive Prompt Engineering Tutorial (GitHub) — 9 chapters with exercises
- Anthropic Prompt Engineering Docs — Official reference
- OpenAI Prompt Engineering Guide — Official OpenAI guide
- PromptingGuide.ai — Comprehensive techniques
Structured Outputs / JSON Schemas
- OpenAI Structured Outputs Guide — Force models to match schemas
- Instructor Library — Cleanest way to get structured outputs via Pydantic
- OpenAI Cookbook: Structured Outputs Intro — Real-world examples
Function / Tool Calling
- OpenAI Function Calling Guide — Definitive reference
- Anthropic Tool Use Docs — Claude's tool calling
- OpenAI Cookbook: Function Calling with Chat Models — Full notebook
Streaming Responses
- OpenAI Streaming Docs — stream=True reference
- Anthropic Streaming Docs — Python examples
- How Streaming LLM APIs Work — Simon Willison — Technical breakdown
Conversation State
- OpenAI Chat Completions: Managing Conversations — Messages array explained
- Anthropic Messages API Docs — Same concept, different syntax
Cost, Latency & Tokens
- OpenAI Pricing — Cost per model
- Anthropic Pricing — Claude model costs
- OpenAI Tokenizer Tool — Interactive token counter
- Tiktoken (Python) — Count tokens in code
Failure Handling
- OpenAI Error Codes Reference — Every error type
- Anthropic Error Handling Docs — Claude errors
- Tenacity (Python) — Retry logic with exponential backoff
Prompt Injection Awareness
- OWASP Top 10 for LLM Apps — Prompt Injection — Authoritative classification
- OWASP Prompt Injection Prevention Cheat Sheet — Defensive patterns
- Evidently AI: What is Prompt Injection — Developer-focused explainer
🔨 Practice Project: Build a multi-tool assistant with get_weather(), calculate(), and search_notes() functions
Month 3: RAG (Retrieval-Augmented Generation)
Goal: Build systems that let LLMs answer questions from your documents with grounded, cited answers.
Embeddings
- Stack Overflow: Intuitive Introduction to Text Embeddings — Best beginner explanation
- Google ML Crash Course: Embeddings — Why dense vectors work
- HuggingFace: Getting Started With Embeddings — Hands-on guide
- OpenAI Embeddings Guide — API reference
Chunking
- Weaviate: Chunking Strategies for RAG — Most practical guide
- Unstructured: Chunking Best Practices — Technical deep-dive
- LangChain Text Splitters — Practical reference
Vector Databases
- Chroma Docs — Perfect for prototyping, runs locally
- Pinecone Learning Center — Great tutorials (provider-agnostic)
- Qdrant Documentation — Best open-source for production
- pgvector — Vector search in PostgreSQL
Metadata Filtering
- Pinecone: Metadata Filtering Guide — Clear code examples
- LlamaIndex: Metadata Filters — Query-time filtering
Reranking
- Cohere Reranking Docs — Best starting point
- LangChain: Cohere Reranker Integration — Wire into LangChain
Retrieval Quality & Hallucination
- LangChain: Query Transformations — Query rewriting, HyDE
- Pinecone: Improving Retrieval Quality — Common failure modes
- Zep: Reducing LLM Hallucinations — Developer guide
- Voiceflow: 5 Ways to Reduce Hallucinations — Combined strategies
Citations & Grounding
- Anthropic: Giving Claude Sources — Cited responses
- LangChain: RAG with Sources — Return source documents
RAG Frameworks
- LlamaIndex: Introduction to RAG — 5 key stages
- LlamaIndex Starter Tutorial — Working RAG in 30 lines
- LangChain: Build a RAG Agent — Full pipeline with reranking
🔨 Practice Project: Build a "chat with your docs" app with FastAPI, vector DB, reranking, and cited answers
Month 4: Agents, Workflows & Evals
Goal: Build AI systems that take autonomous actions, wire multi-step workflows, and evaluate performance.
Agent Loops
- Anthropic: Building Effective Agents — Single best resource on agents
- OpenAI: Practical Guide to Building Agents (PDF) — Patterns and guardrails
- freeCodeCamp: Open Source LLM Agent Handbook — Comprehensive practical guide
- LangChain Academy: Intro to LangGraph — Free course on agent orchestration
Tool Selection
- OpenAI: Function Calling Best Practices — Writing reliable tool descriptions
- Anthropic: Tool Use Best Practices — When to force vs auto-select
State Management
- LangGraph: State Management — Definitive reference
- DataCamp: LangGraph Agents Tutorial — Hands-on fundamentals
- Real Python: LangGraph in Python — Complete stateful agent build
Retries & Failure Handling
- LangGraph: Error Handling and Retries — Tool-node retry logic
- OpenAI Agents Guide: Guardrails — Layered defense
When NOT to Use Agents
- Anthropic: When to Use Agents — Decision framework
- Simon Willison: Designing Agentic Loops — When complexity is justified
Multi-Step Workflows
- Anthropic: Workflow Patterns — Chaining, routing, parallelization
- LangGraph: Multi-Agent Networks — Supervisor and handoff patterns
Evaluation
- DeepEval — Open-source eval framework (pytest-style)
- Promptfoo — CLI for testing LLM apps
- LangSmith — Tracing, debugging, evaluation
- Ragas — RAG-specific evaluation (faithfulness, relevancy)
- Hamel Husain: Your AI Product Needs Evals — Building real eval pipelines
- OpenAI Evals Framework — Community-contributed patterns
🔨 Practice Project: Build a 3-step content pipeline: extract facts → generate posts in parallel → score and pick best
Month 5: Deployment & Production
Goal: Deploy AI apps that handle real users, real traffic, and real failures.
FastAPI Production
- FastAPI Deployment Docs — Uvicorn, Gunicorn, Docker
- FastAPI Production Deployment Guide (CYS) — Nginx, health checks, rate limiting
- FastAPI Best Practices (FastLaunchAPI) — Async pooling, Redis, JWT
Docker
- Docker Getting Started — Images, containers, Compose
- freeCodeCamp: Multi-Agent AI with Docker — End-to-end tutorial
- DataCamp: Deploy LLM Apps with Docker — LLM-specific guide
- Docker for LLM Apps (ApXML) — Multi-stage builds
Background Jobs
- Celery Getting Started — Standard Python task queue
- FastAPI Background Tasks — Lightweight built-in option
Auth & Security
- FastAPI Security Docs — OAuth2, JWT, API keys
- OWASP API Security Top 10 — Authoritative risk list
- Auth0: API Auth Best Practices — Implementation guide
Logging & Observability
- Langfuse — Open-source LLM observability
- LangSmith — LangChain tracing and monitoring
- Python Structlog — Structured JSON logging
Prompt & Version Management
- Langfuse Prompt Management — Centralized prompt versioning
- Anthropic Prompt Management — Best practices at scale
Cost Monitoring
- OpenAI Usage Dashboard — Track spending by model
- Anthropic Usage Dashboard — Claude API usage
- Helicone — Proxy-based cost tracking
- LiteLLM — Unified interface, budget management
Caching
- Redis Docs — Standard in-memory data store
- GPTCache — Semantic caching for LLM apps
🔨 Practice Project: Containerize your RAG app with docker-compose (FastAPI + vector DB + Redis)
Month 6: Specialize & Get Hired
Choose one of three directions and focus on practice.
Direction 1: AI Product Engineer
Best for startup jobs
- Vercel AI SDK — AI-powered UIs with streaming
- Streamlit — Data apps in pure Python
- Gradio — Quick ML/AI interfaces
- Google: People + AI Guidebook — Human-AI interaction design
- Nielsen Norman Group: AI UX — Research-backed guidelines
Direction 2: Applied ML / LLM Engineer
Best for deeper technical roles
- Google ML Crash Course: Fine-tuning vs Prompting — Decision framework
- Codecademy: Prompt Engineering vs Fine-Tuning — Use cases
- IBM: RAG vs Fine-Tuning vs Prompt Engineering — Complete decision space
- OpenAI Fine-tuning Guide — Easiest way to start
- HuggingFace Transformers Fine-tuning — Open-source models
- Unsloth — 2x faster fine-tuning, 80% less memory
- LLaMA-Factory — Fine-tune 100+ LLMs
- Ollama — Run open-source LLMs locally
- HuggingFace Model Hub — Largest model repository
- vLLM — High-throughput LLM serving
- HuggingFace: Optimizing LLM Inference — KV-cache, quantization
- NVIDIA TensorRT-LLM — Max inference performance
Direction 3: AI Automation Engineer
Best for building for businesses immediately
- n8n — Visual workflow automation with AI nodes
- LangGraph: Multi-Agent Workflows — Code-first orchestration
- Temporal — Durable workflow engine
- Zapier AI Actions — Connect AI to 6,000+ apps
- Make (Integromat) — Advanced visual automation
- OpenAI Cookbook: AI Email Processing — Email classification patterns
- LangChain: Document Processing — 80+ document sources
🔨 Practice Project: Build an end-to-end lead qualification system (scrape → research → score → draft outreach → log to CRM)
What AI Engineers Earn
| Level | Salary Range |
|---|---|
| Junior (entry) | $90,000 – $130,000 |
| Mid-level (3-5 years) | $155,000 – $200,000 |
| Senior | $195,000 – $350,000+ |
| Average (Glassdoor) | $184,757 |
Freelance rates:
| Specialty | Rate |
|---|---|
| AI Agent Development | $175 – $300/hr |
| RAG Implementation | $150 – $250/hr |
| LLM Integration | $125 – $200/hr |
Market stats:
- AI job postings grew 25% year-over-year
- 56% wage premium for AI skills vs non-AI roles
- Only 1% of companies are AI-mature
- 26% projected job growth through 2034