CF

Claude Code for Real Engineers

9h 31m 30s
English
Paid

Claude Code for Real Engineers is a 84-lesson 9 hours 31 minutes self-paced course by Matt Pocock. AI changes fast .

Course facts

Lessons
84
Duration
9 hours 31 minutes
Level
All levels
Language
English
Updated
Instructor
Matt Pocock
Price
Premium

AI changes fast. New tools appear each week, and it can be hard to know what skills matter. You may hear bold claims about the future of coding, but you need clear methods and practical skills that help you build real systems.

Why AI work is hard

You can build a small AI demo with a prompt and a few API calls. Real products are different. You must handle unstable output, data leaks, model errors, and weak user flows. These issues show that good AI work is an engineering job, not a quick hack.

This course teaches you how to build stable AI features. You learn how to think like an AI engineer and ship tools that users can trust.

What you learn

You explore core skills used in modern AI products. Each topic has clear examples and code tasks that you can apply at work.

  • Prompt engineering
  • Evals
  • Observability
  • Tracing
  • RAG systems
  • AI agents

You do not need to study model training or GPU setup. You use strong existing models and focus on how to build useful apps around them.

How the course works

You follow a clear path with small coding tasks and real engineering cases. You use Claude Code from the first lesson and build up a full workflow over time.

The course runs in a cohort format. You learn with others, share ideas, and discuss your code. New short lessons and coding tasks appear each week. You also join live Office Hours to ask questions and review hard parts.

Who this course is for

This course suits engineers who want to build AI systems, not just watch demos. If you want to ship real features and understand how to work with modern models, this path is for you.

Claude Code for real engineers.

Who teaches Claude Code for Real Engineers? Matt Pocock

Matt Pocock thumbnail

Matt Pocock is a UK-based developer and the founder of Total TypeScript — one of the most authoritative paid course platforms on the TypeScript language. He was previously a developer-experience engineer at Vercel and is widely cited as one of the clearest teachers of TypeScript's deeper type-system patterns. His Twitter / X presence is one of the largest single-language educational accounts in the JavaScript ecosystem.

His CourseFlix listing carries four Matt Pocock courses: Total TypeScript — Professional TypeScript Training (the platform's flagship comprehensive course), TypeScript Pro Essentials, AI SDK v5 Crash Course, and Build Your Own AI Personal Assistant in TypeScript. The TypeScript material is taught at the level of a working senior engineer who routinely uses the type system as a design tool, not just type annotations.

Material is paid; Total TypeScript runs on per-course pricing on the original platform. Courses are aimed at intermediate-and-up TypeScript developers.

What lessons are included in Claude Code for Real Engineers?

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#1: 001 Where We're Going
All Course Lessons (84)
#Lesson TitleDurationAccess
1
001 Where We're Going Demo
03:01
2
002 Repo Setup
03:16
3
003 Setting Up Claude for the Course
02:08
4
004 How to Take This Course
03:24
5
005 Office Hours
00:57
6
006 Managing Your Claude Code Session
04:07
7
007 Prompting in the Terminal
02:26
8
008 Claude and Your IDE
01:58
9
009 Going Forwards and Backwards in Time
03:05
10
010 Running Bash Commands
03:47
11
011 Permissions
04:16
12
012 The Constraints of LLMs
08:15
13
013 What Are Subagents
02:32
14
014 Codebase Exploration
01:32
15
015 Codebase Exploration SOLUTION
02:47
16
016 Build a Feature
02:02
17
017 Build a Feature SOLUTION
08:11
18
018 Showing Context in the Status Line
03:36
19
019 What Is Plan Mode
03:12
20
020 The Plan Execute Clear Loop
01:43
21
021 The Plan Execute Clear Loop SOLUTION
09:36
22
022 Compaction
07:57
23
023 What Is an Agents MD File
06:23
24
024 Steering an Agent with the Agents MD File
02:20
25
025 Steering an Agent with the Agents MD File SOLUTION
03:36
26
026 Progressive Disclosure
03:18
27
027 What Are Agent Skills
06:58
28
028 A Skill for Writing Skills
01:48
29
029 A Skill for Writing Skills SOLUTION
06:43
30
030 Automatic Memory
02:13
31
031 How to Tackle Massive Tasks
03:28
32
032 Write Great PRDs with This Skill
02:58
33
033 Write Great PRDs with This Skill SOLUTION
09:53
34
034 Split Features Across Multiple Context Windows with Multi Phase Plans
00:59
35
035 Split Features Across Multiple Context Windows with Multi Phase Plans SOLUTION
03:40
36
036 What Are Tracer Bullets
03:10
37
037 Use Tracer Bullets in Our Multi Phase Plan
01:10
38
038 Use Tracer Bullets in Our Multi Phase Plan SOLUTION
03:56
39
039 Executing Our Multi Phase Plan
01:22
40
040 Executing Our Multi Phase Plan SOLUTION
06:36
41
041 The _grill-me Skill
02:07
42
042 Claude Code for Real Engineers Office Hours Mon Mar 30 0830 UTC
47:01
43
043 Claude Code for Real Engineers Office Hours Mon Mar 30 1530 UTC
49:51
44
044 Claude Code for Real Engineers Office Hours Fri Apr 3 08:30 UTC
48:26
45
045 Claude Code for Real Engineers Office Hours Fri Apr 3 15:30 UTC
48:06
46
046 Is Code Cheap.mp4
06:05
47
047 Steering Agents to Use Feedback Loops with Skills.mp4
03:21
48
048 Building a Do Work Skill.mp4
00:46
49
049 Building a Do Work Skill Solution.mp4
03:06
50
050 Using Our Do Work Skill.mp4
01:06
51
051 Using Our Do Work Skill Solution.mp4
03:03
52
052 Fixing Agents Broken Formatting with Pre Commit.mp4
03:52
53
053 What Is Red Green Refactor.mp4
03:43
54
054 Red Green Refactor.mp4
01:35
55
055 Red Green Refactor Solution.mp4
04:30
56
056 What Is Ralph.mp4
04:09
57
057 HITL vs AFK Ralph.mp4
01:33
58
058 Trying HITL Ralph.mp4
03:30
59
059 Trying HITL Ralph Solution.mp4
02:13
60
060 Sandboxing.mp4
04:18
61
061 Setting Up and Trying AFK Ralph.mp4
02:47
62
062 Setting Up and Trying AFK Ralph Solution.mp4
03:21
63
063 Using Backlogs to Queue Tasks for Ralph.mp4
03:29
64
064 Setting Up Our Repo for GitHub Issues.mp4
03:04
65
065 Hooking Up Ralph to Your Backlog.mp4
05:28
66
066 Hooking Up Ralph to Your Backlog Solution.mp4
04:20
67
067 Updating Our PRD and Plan Skill to Use GitHub.mp4
01:10
68
068 HITL and AFK Tasks.mp4
02:06
69
069 Dont Plan Kanban.mp4
03:44
70
070 Using the Kanban Skill.mp4
01:09
71
071 Using the Kanban Skill Solution.mp4
05:24
72
072 Research.mp4
03:14
73
073 Trying Out Research.mp4
01:46
74
074 Trying Out Research Solution.mp4
04:27
75
075 Prototyping.mp4
02:57
76
076 Trying Out Prototyping.mp4
01:35
77
077 Trying Out Prototyping Solution.mp4
03:14
78
078 Designing Codebases AI Loves.mp4
07:31
79
079 The Improve My Codebase Skill.mp4
02:22
80
080 The Improve My Codebase Skill Solution.mp4
04:57
81
081 Adding Module Awareness to Our Plan PRD Skill.mp4
01:34
82
082 Claude Code for Real Engineers Office Hours, Fri Apr 10, 0830 UTC
49:26
83
083 Claude Code for Real Engineers Office Hours, Fri Apr 10, 1530 UTC
49:11
84
084 Sandcastle Day 5 Ralph
06:34
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Frequently asked questions

What prerequisites are needed to enroll in the course?
The course is designed for engineers who are interested in building AI systems. Participants should have a foundational understanding of coding and software development principles. Familiarity with basic programming concepts and tools, such as using an IDE and running bash commands, will be beneficial. Prior experience with AI is not required as the course focuses on using existing models rather than training new ones.
What practical skills will I develop in this course?
You will learn core skills essential for modern AI product development, including prompt engineering, evaluations (evals), observability, tracing, Retrieval-Augmented Generation (RAG) systems, and working with AI agents. These skills are taught through practical examples and coding tasks that apply directly to real-world scenarios, enhancing your capability to build reliable and user-friendly AI applications.
How is this course structured compared to other AI courses?
Unlike many AI courses that focus on theoretical knowledge or model training, this course emphasizes practical engineering skills needed to build stable AI features. It adopts a hands-on approach with a series of small coding tasks and real engineering cases. The cohort format allows for collaborative learning through shared ideas and live Office Hours, distinguishing it from self-paced, isolated learning experiences.
Which specific tools or platforms are used throughout the course?
The course centers around the use of Claude Code, an AI coding tool. You will engage with various tools and concepts such as the IDE for code integration, bash commands for terminal operations, and specific workflows like Plan Execute Clear Loop and Multi Phase Plans. These tools support the development of AI systems through structured guidance and practical applications.
What topics are not covered in this course?
This course does not delve into model training or GPU setup. It focuses on applying strong existing AI models to build applications, rather than creating new models from scratch. As such, participants will work with pre-existing models and concentrate on the engineering aspects of integrating AI into functional systems.
How much time should I expect to commit to this course?
The course consists of 84 lessons, with new lessons and coding tasks released each week. Participants are expected to engage with these tasks and participate in live Office Hours for discussions and queries. The time commitment will vary based on individual pace, but consistent weekly engagement will be necessary to fully benefit from the course content and collaborative learning opportunities.
How will the skills learned in this course benefit my career?
The practical engineering skills acquired in this course, such as prompt engineering, observability, and working with AI agents, are highly relevant to the current AI job market. These skills are transferable across various AI product development roles, enhancing your ability to build robust and user-friendly AI applications, thereby broadening career opportunities in the emerging field of AI engineering.