Build practical programming and technology skills

AI Engineering

Build reliable AI-powered applications with model interfaces, structured outputs, retrieval, evaluation, safety controls, privacy, observability, and cost management.

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ProfessionalDifficulty

50 hoursEstimated time

8 modules32 lessons

5 guided labsKnowledge check

Plan your learning

Path Brief

Skills you will practice

Data Ai · Ai Engineering
Data Ai · Ai Application Architecture
Data Ai · Model And Provider Interfaces
Data Ai · Prompt And Context Design
Data Ai · Retrieval Augmented Applications
Data Ai · Tools And Structured Outputs

Prerequisites

Python Fundamentals
Http Apis
Software Testing Fundamentals

Tools

A current web browser
A code editor or terminal appropriate to the path
A safe local or virtual lab environment

Before you begin

What you need

Python Fundamentals
Http Apis
Software Testing Fundamentals

Step-by-step curriculum

Learning modules

Complete lessons in order or open the module that matches your current goal. Your lesson progress is saved on this device.

01AI Application ArchitectureBuild practical skill in ai application architecture and connect it to the outcomes of AI Engineering.4 lessons · 1 lab

Lesson 1

AI Application Architecture: Core Concepts for AI Engineering

Understand the concepts, decisions, and vocabulary you need before applying AI Application Architecture in AI Engineering.

25 min

Lesson 2

Set Up and Explore AI Application Architecture in AI Engineering

Prepare the tools, data, project state, or test environment needed to explore AI Application Architecture safely and repeatably.

25 min

Lesson 3

Build a Practical AI Application Architecture Example in AI Engineering

Build a small AI workflow with a fixed evaluation set and record output quality, failure cases, latency, and cost-related observations.

30 min

Lesson 4

Debug Common AI Application Architecture Problems in AI Engineering

Recognize common failure modes in AI Application Architecture, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Safe

Guided Lab: AI Application Architecture in AI Engineering

AI Engineering — AI Application Architecture lab: Build a small AI workflow with a fixed evaluation set and record output quality, failure cases, latency, and cost-related observations.

Not started

02Model and Provider InterfacesBuild practical skill in model and provider interfaces and connect it to the outcomes of AI Engineering.4 lessons · 1 lab

Lesson 1

Model and Provider Interfaces: Core Concepts for AI Engineering

Understand the concepts, decisions, and vocabulary you need before applying Model and Provider Interfaces in AI Engineering.

25 min

Lesson 2

Set Up and Explore Model and Provider Interfaces in AI Engineering

Prepare the tools, data, project state, or test environment needed to explore Model and Provider Interfaces safely and repeatably.

25 min

Lesson 3

Build a Practical Model and Provider Interfaces Example in AI Engineering

Create a small domain type with validation, one behavior method, and a test that protects an invariant.

30 min

Lesson 4

Debug Common Model and Provider Interfaces Problems in AI Engineering

Recognize common failure modes in Model and Provider Interfaces, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Moderate

Guided Lab: Model and Provider Interfaces in AI Engineering

AI Engineering — Model and Provider Interfaces lab: Create a small domain type with validation, one behavior method, and a test that protects an invariant.

Not started

03Prompt and Context DesignBuild practical skill in prompt and context design and connect it to the outcomes of AI Engineering.4 lessons · 1 lab

Lesson 1

Prompt and Context Design: Core Concepts for AI Engineering

Understand the concepts, decisions, and vocabulary you need before applying Prompt and Context Design in AI Engineering.

25 min

Lesson 2

Set Up and Explore Prompt and Context Design in AI Engineering

Prepare the tools, data, project state, or test environment needed to explore Prompt and Context Design safely and repeatably.

25 min

Lesson 3

Build a Practical Prompt and Context Design Example in AI Engineering

Build a small AI workflow with a fixed evaluation set and record output quality, failure cases, latency, and cost-related observations.

30 min

Lesson 4

Debug Common Prompt and Context Design Problems in AI Engineering

Recognize common failure modes in Prompt and Context Design, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Moderate

Guided Lab: Prompt and Context Design in AI Engineering

AI Engineering — Prompt and Context Design lab: Build a small AI workflow with a fixed evaluation set and record output quality, failure cases, latency, and cost-related observations.

Not started

04Retrieval-Augmented ApplicationsBuild practical skill in retrieval-augmented applications and connect it to the outcomes of AI Engineering.4 lessons · 1 lab

Lesson 1

Retrieval-Augmented Applications: Core Concepts for AI Engineering

Understand the concepts, decisions, and vocabulary you need before applying Retrieval-Augmented Applications in AI Engineering.

25 min

Lesson 2

Set Up and Explore Retrieval-Augmented Applications in AI Engineering

Prepare the tools, data, project state, or test environment needed to explore Retrieval-Augmented Applications safely and repeatably.

25 min

Lesson 3

Build a Practical Retrieval-Augmented Applications Example in AI Engineering

Build a small AI workflow with a fixed evaluation set and record output quality, failure cases, latency, and cost-related observations.

30 min

Lesson 4

Debug Common Retrieval-Augmented Applications Problems in AI Engineering

Recognize common failure modes in Retrieval-Augmented Applications, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Moderate

Guided Lab: Retrieval-Augmented Applications in AI Engineering

AI Engineering — Retrieval-Augmented Applications lab: Build a small AI workflow with a fixed evaluation set and record output quality, failure cases, latency, and cost-related observations.

Not started

05Tools and Structured OutputsBuild practical skill in tools and structured outputs and connect it to the outcomes of AI Engineering.4 lessons · 1 lab

Lesson 1

Tools and Structured Outputs: Core Concepts for AI Engineering

Understand the concepts, decisions, and vocabulary you need before applying Tools and Structured Outputs in AI Engineering.

25 min

Lesson 2

Set Up and Explore Tools and Structured Outputs in AI Engineering

Prepare the tools, data, project state, or test environment needed to explore Tools and Structured Outputs safely and repeatably.

25 min

Lesson 3

Build a Practical Tools and Structured Outputs Example in AI Engineering

Build a small AI workflow with a fixed evaluation set and record output quality, failure cases, latency, and cost-related observations.

30 min

Lesson 4

Debug Common Tools and Structured Outputs Problems in AI Engineering

Recognize common failure modes in Tools and Structured Outputs, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Moderate

Guided Lab: Tools and Structured Outputs in AI Engineering

AI Engineering — Tools and Structured Outputs lab: Build a small AI workflow with a fixed evaluation set and record output quality, failure cases, latency, and cost-related observations.

Not started

06Evaluation and Test DatasetsBuild practical skill in evaluation and test datasets and connect it to the outcomes of AI Engineering.4 lessons

Lesson 1

Evaluation and Test Datasets: Core Concepts for AI Engineering

Understand the concepts, decisions, and vocabulary you need before applying Evaluation and Test Datasets in AI Engineering.

25 min

Lesson 2

Set Up and Explore Evaluation and Test Datasets in AI Engineering

Prepare the tools, data, project state, or test environment needed to explore Evaluation and Test Datasets safely and repeatably.

25 min

Lesson 3

Build a Practical Evaluation and Test Datasets Example in AI Engineering

Store a small dataset in an appropriate collection, update it, search it, and explain why the chosen structure fits.

30 min

Lesson 4

Debug Common Evaluation and Test Datasets Problems in AI Engineering

Recognize common failure modes in Evaluation and Test Datasets, use the relevant diagnostics, and verify the correction.

25 min

07Safety, Privacy, and GuardrailsBuild practical skill in safety, privacy, and guardrails and connect it to the outcomes of AI Engineering.4 lessons

Lesson 1

Safety, Privacy, and Guardrails: Core Concepts for AI Engineering

Understand the concepts, decisions, and vocabulary you need before applying Safety, Privacy, and Guardrails in AI Engineering.

25 min

Lesson 2

Set Up and Explore Safety, Privacy, and Guardrails in AI Engineering

Prepare the tools, data, project state, or test environment needed to explore Safety, Privacy, and Guardrails safely and repeatably.

25 min

Lesson 3

Build a Practical Safety, Privacy, and Guardrails Example in AI Engineering

Build a small AI workflow with a fixed evaluation set and record output quality, failure cases, latency, and cost-related observations.

30 min

Lesson 4

Debug Common Safety, Privacy, and Guardrails Problems in AI Engineering

Recognize common failure modes in Safety, Privacy, and Guardrails, use the relevant diagnostics, and verify the correction.

25 min

08Reliability, Cost, and ObservabilityBuild practical skill in reliability, cost, and observability and connect it to the outcomes of AI Engineering.4 lessons

Lesson 1

Reliability, Cost, and Observability: Core Concepts for AI Engineering

Understand the concepts, decisions, and vocabulary you need before applying Reliability, Cost, and Observability in AI Engineering.

25 min

Lesson 2

Set Up and Explore Reliability, Cost, and Observability in AI Engineering

Prepare the tools, data, project state, or test environment needed to explore Reliability, Cost, and Observability safely and repeatably.

25 min

Lesson 3

Build a Practical Reliability, Cost, and Observability Example in AI Engineering

Add one log, metric, and health signal to a small service and use them to diagnose a controlled failure.

30 min

Lesson 4

Debug Common Reliability, Cost, and Observability Problems in AI Engineering

Recognize common failure modes in Reliability, Cost, and Observability, use the relevant diagnostics, and verify the correction.

25 min

Portfolio integration

Capstone Project

Build and Evaluate a Source-Grounded AI Assistant

Combine the path skills in a portfolio-ready project with clear functional requirements, tests, verification evidence, and operating notes.

GuidedIndependentPortfolio

Acceptance criteria

The core workflow works from start to finish.
Invalid input or failure states are handled clearly.
The result is tested and documented.
A reviewer can reproduce the setup and verification.

Continue in Programming

Related Programming

Skill assessment

AI Engineering

Path Brief

Skills you will practice

Prerequisites

Tools

What you need

Learning modules

AI Application Architecture: Core Concepts for AI Engineering

Set Up and Explore AI Application Architecture in AI Engineering

Build a Practical AI Application Architecture Example in AI Engineering

Debug Common AI Application Architecture Problems in AI Engineering

Guided Lab: AI Application Architecture in AI Engineering

Model and Provider Interfaces: Core Concepts for AI Engineering

Set Up and Explore Model and Provider Interfaces in AI Engineering

Build a Practical Model and Provider Interfaces Example in AI Engineering

Debug Common Model and Provider Interfaces Problems in AI Engineering

Guided Lab: Model and Provider Interfaces in AI Engineering

Prompt and Context Design: Core Concepts for AI Engineering

Set Up and Explore Prompt and Context Design in AI Engineering

Build a Practical Prompt and Context Design Example in AI Engineering

Debug Common Prompt and Context Design Problems in AI Engineering

Guided Lab: Prompt and Context Design in AI Engineering

Retrieval-Augmented Applications: Core Concepts for AI Engineering

Set Up and Explore Retrieval-Augmented Applications in AI Engineering

Build a Practical Retrieval-Augmented Applications Example in AI Engineering

Debug Common Retrieval-Augmented Applications Problems in AI Engineering

Guided Lab: Retrieval-Augmented Applications in AI Engineering

Tools and Structured Outputs: Core Concepts for AI Engineering

Set Up and Explore Tools and Structured Outputs in AI Engineering

Build a Practical Tools and Structured Outputs Example in AI Engineering

Debug Common Tools and Structured Outputs Problems in AI Engineering

Guided Lab: Tools and Structured Outputs in AI Engineering

Evaluation and Test Datasets: Core Concepts for AI Engineering

Set Up and Explore Evaluation and Test Datasets in AI Engineering

Build a Practical Evaluation and Test Datasets Example in AI Engineering

Debug Common Evaluation and Test Datasets Problems in AI Engineering

Safety, Privacy, and Guardrails: Core Concepts for AI Engineering

Set Up and Explore Safety, Privacy, and Guardrails in AI Engineering

Build a Practical Safety, Privacy, and Guardrails Example in AI Engineering

Debug Common Safety, Privacy, and Guardrails Problems in AI Engineering

Reliability, Cost, and Observability: Core Concepts for AI Engineering

Set Up and Explore Reliability, Cost, and Observability in AI Engineering

Build a Practical Reliability, Cost, and Observability Example in AI Engineering

Debug Common Reliability, Cost, and Observability Problems in AI Engineering

Capstone Project

Build and Evaluate a Source-Grounded AI Assistant

Acceptance criteria

Test your understanding

Related Programming

Check your starting point

Plan the capstone

Explore related paths