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AI & Machine Learning All levels

Claude and ChatGPT for Everyday Engineering Work

Make assistants a genuine part of your workflow, not a party trick abandoned by Friday.

Rated 4.0 out of 5 from 1 review 23 students

LM Created by Laura Mbeki

  • Last updated August 2026
  • English
  • 7h of material
  • 52 lessons

What you will learn

8 concrete outcomes

Every bullet below is something you will have built, shipped or be able to explain by the time you finish the last lesson.

  • Judge within seconds whether a task is a good fit for a model
  • Understand an unfamiliar codebase far faster than by reading alone
  • Generate adapter code, migrations and test cases you would rather not hand-write
  • Verify output cheaply, and know which answers you must run before trusting
  • Make a model argue against its own answer to expose weak reasoning
  • Keep reusable project context so you stop re-explaining your system
  • Choose between editor-integrated and browser-based assistants deliberately
  • Handle AI-assisted work honestly in a shared repository and with clients

Course curriculum

7 modules · 52 lessons · 7h of material

7 lessons running 54m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

8 lessons running 1h 4m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

8 lessons running 1h 8m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

8 lessons running 1h 8m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

7 lessons running 58m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

7 lessons running 56m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

7 lessons running 52m in total. Each lesson ships with the finished source files and a short written recap, so you can follow along in your own editor and skim the module again later.

Lesson-by-lesson titles, code downloads and exercises live inside the course library you get access to straight after checkout.

7 modules · 52 lessons

7h total length

Requirements

Short list, and deliberately so. If you meet these you can start today.

  • Professional or hobby experience writing software in any language
  • An account with Claude or ChatGPT on any tier

About this course

Almost every developer has tried an assistant and almost none have made it stick. The pattern is familiar: a spectacular first week, a growing suspicion that verifying the output costs more than writing it, and a quiet return to old habits. This course is the antidote.

The organising idea is fit. Some tasks are a superb fit for a model and some are actively worse with one, and the difference is predictable rather than mysterious. You will learn to recognise both within seconds. Then you work through the tasks that genuinely fit: understanding an unfamiliar codebase, writing the boring adapter layer, translating between languages, generating test cases, reviewing your own diff, drafting documentation, and untangling a configuration file that someone left behind in 2019.

Verification gets its own long module, because trusting output you have not checked is how teams get burned. You will learn cheap checks that catch most errors, when to demand a citation, how to make a model contradict itself deliberately, and which categories of answer to never accept without running.

The course finishes with workflow: assistants in the editor against assistants in a browser tab, keeping project context reusable, the etiquette of AI-assisted work in a shared repository, and an honest section on what to disclose to your employer and clients.

Topics covered

Frequently asked questions

Still unsure about something? Write to misteryjj100@gmail.com and a human answers, usually the same working day.

No. Everything works on free tiers, though the longer-context exercises are more comfortable on a paid plan. Where a paid feature matters, the lesson says so.

Partly, but the bigger gains come from comprehension, verification and the tedious work around the code. Those get the most time in this course.

The judgement of fit and the verification habits outlast any model release. Tool-specific lessons are revised regularly and updates reach your access link automatically.

Checkout is handled on our provider's secure payment page. The moment your payment clears we email your personal access link and access code to the address you used at checkout, and the same link appears in your account library. There is nothing to install and nothing to wait for.

Email misteryjj100@gmail.com within 14 days of your purchase, quote your order number, and we refund the full amount to your original payment method. No form to fill in and no questions about how much of the course you watched.

What students say

Reviews are written by people who bought this course. We publish the critical ones too.

4.0

Rated 4.0 out of 5

Course rating · 1 review

Rating distribution

  • 5 stars 0%
  • 4 stars 100%
  • 3 stars 0%
  • 2 stars 0%
  • 1 star 0%
  • GS

    Gemma Sinclair

    Senior developer

    Jan 2026
    Rated 4.0 out of 5

    Level-headed, occasionally obvious

    The chapter on what these tools are genuinely poor at is worth more than most of what has been written on the subject, and the reusable project context idea has stayed with me. Some of the middle sections tell an experienced developer things they already do without thinking. Even so, nothing else I have read on this keeps its head half as well.

Your instructor

LM

Laura Mbeki

Prompt engineer and AI workflow designer

  • 506 students taught
  • 16 courses published
  • 4.3 instructor rating
  • Prompt engineering
  • Prompt libraries
  • AI workflows
  • Prompt evaluation

Laura went independent after seven years of agency work and now designs the prompt libraries that sit behind other people's products. She treats prompting as engineering: versioned prompts, a held-out evaluation set, a regression run before anything ships, and a token budget you have to hit. Her packs are the ones she uses with her own clients — briefing, rewriting, summarising, review — rather than sanitised examples, and each comes with notes on where it fails. She keeps every pack working across ChatGPT, Claude and a small open model, so the technique outlives the model.

See every course by Laura Mbeki
$49 USD

One-time payment · lifetime access

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