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Prompt Engineering Advanced

Guardrails, Refusals and Safe Prompting in Production

Ship model features that refuse the right things and never leak your system instructions.

Rated 4.0 out of 5 from 1 review 8 students

LM Created by Laura Mbeki

  • Last updated August 2026
  • English
  • 7h 50m of material
  • 59 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.

  • Map the real failure surface of a production model feature
  • Keep retrieved and user content as data rather than instruction
  • Defend against indirect injection arriving through documents you retrieved
  • Design refusals that offer the next best step instead of a flat no
  • Balance safety against over-refusal that drives legitimate users away
  • Validate output against policy before it reaches a customer
  • Run a red-team suite in continuous integration and fail the build on a breach
  • Respond to an incident where a bad answer already reached a user

Course curriculum

7 modules · 59 lessons · 7h 50m of material

8 lessons running 1h 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.

9 lessons running 1h 10m 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.

9 lessons running 1h 14m 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.

9 lessons running 1h 16m 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 6m 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 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 · 59 lessons

7h 50m total length

Requirements

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

  • You have a model feature in production or close to it
  • Comfortable writing Python and reading logs
  • Familiarity with prompt composition and testing concepts

About this course

Once a model feature has real users, a new category of bug appears. It is not a crash and not a wrong answer. It is the assistant confidently promising a refund your company does not offer, reciting its own system prompt to a curious user, or answering a question about a competitor in a way legal would rather it had not.

This course treats that category seriously. You map the failure surface first: instruction leakage, jailbreak patterns, indirect injection through retrieved documents, over-refusal that annoys legitimate users, tone failures, and the commitments a model can make on your behalf.

Then you build defences in layers. Input classification before the expensive call, instruction hierarchy that keeps untrusted content as data, output validation with a policy checker, blocked patterns for identifiers and secrets, and a refusal design that stays useful by offering the next best step instead of a flat no. A dedicated module covers indirect injection through retrieval, which is the failure most teams have not yet thought about.

The last part is operational: red-team suites that run in continuous integration, incident response for a bad answer that reached a customer, human escalation paths, and the logging and retention questions that follow. You leave with a guardrail layer and a red-team suite you can point at your own system.

Topics covered

Frequently asked questions

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

It is an applied one. It assumes no formal security background but takes an adversarial view throughout, and the red-team module is genuinely hands-on.

That is the exact trade the course measures. Over-refusal is treated as a defect with its own tests, not as an acceptable cost of being careful.

It covers the engineering that compliance depends on: logging, retention, escalation and audit trails. It is not legal advice and says so plainly.

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%
  • AF

    Aurélien Faure

    Security engineer

    Dec 2025
    Rated 4.0 out of 5

    Indirect injection is the standout chapter

    Demonstrating an attack that arrives inside a retrieved document rather than through the user's own message, and then defending against it properly, is material I could not find anywhere else. Four rather than five only because the red-team suite at the end is small and I wanted more adversarial cases to build from.

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
$59 USD

One-time payment · lifetime access

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