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AI Engineering for Software Engineers

Free

A complete transition course for software engineers who want to become AI engineers. The course is structured as an explicit career transition path across four skill shifts: from deterministic thinking to probabilistic systems, from coding assistance to feature architecture, from prompt writing to system evaluation, and from demos to production reliability. Module 1 covers the transition mindset and role expectations. Module 2 builds AI tooling into your daily development workflow. Module 3 teaches prompting for application reliability, not chat. Module 4 covers AI system design: when to use prompting, RAG, agents, local models, or workflow automation. Module 5 adds evaluation and debugging as a first-class skill. Module 6 covers shipping to production and ends with a full end-to-end capstone. By the end, you will be able to design, implement, evaluate, harden, and explain AI systems the way an AI engineer does.

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Complete lessons in order to unlock the next — structured progression.

The Transition to AI Engineering

Understand what changes when you move from traditional software engineering to AI engineering. Learn the new mindset, the developer habits that carry over and the ones that need updating, and what the AI engineering role actually looks like day to day including what skills matter most for interviews and portfolios.

  1. 1Transition From Software Engineer To Ai Engineer: What ChangesTutorial
  2. 2The Developer'S Ai Mindset: What'S Real, What'S Hype, And How To Use It WellTutorial
  3. 3What Ai Engineers Actually Do: Role, Skills, And How To Get ThereTutorial
  4. 4Transition CheckQuiz

AI-Augmented Engineering Workflow

Build AI tool use into your daily development workflow. Use copilots, repo-aware chat, and agentic task execution to write, review, test, and document code faster. Learn which mode to use for which task, how to structure your repo for better AI suggestions, and how to review AI-generated code safely.

  1. 5Set Up Codeium: Free Ai Code Completion In Any EditorTutorial
  2. 6Set Up Cursor For Full Codebase ChatTutorial
  3. 7Use Ai In Your Code EditorTask
  4. 8Ai Code Review And RefactoringTask
  5. 9Generate Tests From Code With AiTask
  6. 10Write Code DocumentationTask
  7. 11Ai Augmented Workflow CheckQuiz

Prompting for Applications, Not Chat

Get reliable, validated, structured output from LLM APIs. Goes beyond conversational prompting to cover the patterns that matter in application code: role and context, structured output with schema enforcement, function calling and tool use, prompt testing, and common failure modes.

  1. 12Prompt Engineering: Role, Context, And StructureTutorial
  2. 13Extract Structured Data From Documents With ClaudeTutorial
  3. 14Function Calling And Tool Use: Making Llms Take ActionsTutorial
  4. 15Extract Structured Data From DocumentsTask
  5. 16Prompting For Applications CheckQuiz

Designing AI Systems and Features

Learn how to choose the right architecture for any AI feature. Covers the six core AI system patterns (plain prompting, structured extraction, RAG, agents, local models, and workflow automation), the tradeoffs between them, and a decision framework for picking the right one. Includes hands-on implementation of RAG pipelines and agents.

  1. 17Ai System Design Patterns: When To Use Prompting, Rag, Agents, And Local ModelsTutorial
  2. 18Install Ollama And Run Llms Locally In 5 MinutesTutorial
  3. 19Set Up Rag In Open Webui: Query Your DocumentsTutorial
  4. 20Build Rag PipelineTask
  5. 21Build Ai Agents And Multi Agent WorkflowsTask
  6. 22Ai Systems Design CheckQuiz

Evaluation and Debugging

Learn how to measure whether an AI feature is actually working and how to diagnose it when it is not. Covers golden test sets, eval datasets, output scoring, retrieval quality checks, agent failure tracing, and a structured approach to debugging each category of AI failure.

  1. 23Evaluating Ai Features: Golden Tests, Eval Datasets, And Quality ScoringTutorial
  2. 24Debugging Ai Failures: How To Diagnose And Fix Broken Ai BehaviorTutorial
  3. 25Evaluation And Debugging CheckQuiz

Shipping AI to Production

Handle the engineering work that turns a working AI feature into one that can run reliably for real users at scale. Covers retries and backoff, cost control, streaming for perceived latency, prompt injection defense, schema enforcement, observability, and rollout strategy. Ends with a full end-to-end capstone.

  1. 26Shipping Ai Features To Production: The Six Things That Will BreakTutorial
  2. 27Multi Model Routing And FallbacksTask
  3. 28Build A Full Stack App With AiTask
  4. 29Deploy Ai ModelsTask
  5. 30Build And Ship One Ai Feature End To EndTutorial
  6. 31Production CheckQuiz

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