From Dev
to AI Engineer
13 modules · 1 real project · built in public
Learn along → Course outlineAll 13 modules
The same path as a plain list. Every module ends with something you build and ship.
M01 Python & Software Engineering for AI
Write clean, typed, tested Python and set up a project the way a professional does.
You ship: A uv-managed Python package with tests, logging and Git history: the skeleton of the AI service.
M02 AI Backend Development
Build and test a FastAPI service that calls an LLM.
You ship: POST /api/chat, /api/summarize and /api/extract: validated, logged, documented, on GitHub.
M03 Data, Machine Learning & LLM Foundations
Explain tokens, context windows, embeddings and attention in plain language.
You ship: Two short design notes: how tokens limit the assistant, and why embeddings beat keyword search.
M04 Building Applications with LLMs
Make LLM calls reliable: structured output, streaming, retries and rate limits.
You ship: Structured JSON answers (answer, sources, confidence) with streaming, retries and basic rate limiting.
M05 Prompt, Context & Harness Engineering
Design prompts and context so the assistant cites sources and admits when it does not know.
You ship: A rewritten system prompt with few-shot examples and a before/after comparison.
M06 Grounding Models with Data & RAG
Build retrieval-augmented generation on PostgreSQL with pgvector.
You ship: A pipeline that chunks a mock knowledge base, embeds it, retrieves the top results and answers with citations.
M07 Tool Calling, MCP & Agentic Systems
Let the model act: call tools, and expose them over MCP.
You ship: One real tool (look up an error code) through function calling, optionally as a minimal MCP server.
M08 Evaluation-Driven AI Development
Measure quality with data instead of feelings.
You ship: 15-20 golden questions and a script that scores the pipeline.
M09 Coding Agents & AI-Assisted Software Engineering
Use AI coding agents deliberately and safely on a real repository.
You ship: An AGENTS.md / CLAUDE.md, plus one real feature built with an agent and reviewed like a pull request.
M10 Shaping the Build & AI System Design
Frame a problem and justify design decisions.
You ship: One architecture decision record: why RAG, why pgvector, and one thing deliberately not built.
M11 AI Security, Guardrails & Governance
Spot and block prompt injection, data leakage and tool abuse.
You ship: An allow-list guardrail for tool calls and an audit log of questions, sources and tool calls.
M12 LLMOps & Operating AI Systems in Production
Operate an AI system in production: tracing, cost, latency and feedback.
You ship: A dashboard with question count, success rate, response time, AI cost and failed queries.
M13 Production Deployment, AI Infrastructure & Model Serving
Package and deploy the whole system.
You ship: One command (docker compose up) for React, ASP.NET Core, Python AI and PostgreSQL, plus a CI workflow.