2 Week Prompt Engineering Certification

2 Week Prompt Engineering Certification

2 Week Prompt Engineering Certification
 - Master practical prompt patterns to get clearer, faster, and more reliable results from AI tools.

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Description

This course contains the use of artificial intelligence.

The 2 Week Prompt Engineering Certification is designed to help learners master one of the most important skills in the age of Generative AI: knowing how to communicate clearly, strategically, and effectively with Large Language Models. Whether you use tools like ChatGPT, Claude, Gemini, Copilot, or AI-powered workplace platforms, the quality of your results depends heavily on the quality of your prompts.

This course begins by explaining how LLMs actually work without unnecessary hype or technical confusion. You will learn the basics of tokens, probabilities, model behavior, and why small changes in wording can dramatically affect AI output. Instead of treating AI like magic, you will understand how to guide it with structure, context, examples, and constraints.

In Week 1, you will build a strong foundation in prompt engineering fundamentals. You will learn the anatomy of a great prompt, including role, task, context, format, tone, length, and output requirements. You will practice turning vague requests into clear, structured instructions that produce more useful responses. The course also introduces core prompt patterns such as zero-shot prompting, few-shot prompting, instruction-based prompting, reusable templates, and structured output design.

You will also learn how to control output quality by setting expectations for tone, format, detail level, audience, and constraints. When prompts fail, you will learn how to debug them using practical iteration frameworks. Through before-and-after examples, you will see how weak prompts can be transformed into high-performing prompts. The Week 1 lab, Prompt Makeover Sprint, gives you hands-on practice improving messy prompts and producing stronger AI outputs.

In Week 2, the course moves into advanced techniques and real-world applications. You will explore reasoning prompts, step-by-step thinking structures, and when reasoning-based prompting is useful or risky. You will learn how to use role-based prompting and persona prompting to guide AI as an analyst, executive, teacher, engineer, researcher, or creative partner.

The course also shows how to prompt for different tasks, including writing, coding, research, summarization, brainstorming, planning, and workflow support. You will learn how to create reusable prompt templates and build prompt-driven workflows that save time across repeated tasks. By the end of the course, you will understand how to combine multiple prompts into practical AI systems for real workplace use.

The final lab, Build Your Prompt OS, helps you create a reusable personal or professional prompt system that can support your daily work. You will leave with a practical toolkit of prompts, templates, frameworks, and workflows that you can immediately apply to business, content creation, learning, coding, research, productivity, and automation.

By completing this certification, you will gain the confidence to use AI tools more effectively, reduce poor outputs, improve productivity, and build a repeatable process for getting high-quality results from modern Generative AI systems.
Who this course is for:

    Beginners who want to learn how to use AI tools more effectively.
    Professionals who want better results from ChatGPT, Claude, Gemini, Copilot, or workplace AI tools.
    Students, educators, creators, marketers, business owners, and knowledge workers who use AI for everyday tasks.
    Anyone who wants to improve productivity, writing, research, planning, summarization, and brainstorming with AI.
    Non-technical learners who want practical AI skills without needing to code.
    Teams and professionals who want reusable prompt templates for business workflows.
    AI enthusiasts who want to move from random prompting to structured, repeatable prompt engineering.

AI-Driven Java Development with TDD and Claude Code

AI-Driven Java Development with TDD and Claude Code

AI-Driven Java Development with TDD and Claude Code
 - Build reliable Spring Boot features using specs, tests, architecture guardrails and Java 21

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EARLY ACCESS — This course is actively being extended. You currently get 62 lectures covering the complete AI+TDD workflow end-to-end on a real feature. New sections are being added over the coming weeks — enrol now and get everything at the early access price.

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Most AI coding courses teach you to generate code. This one teaches you to engineer it.

AI can write code quickly. Trusting that code is a different problem. In real projects, it often misses edge cases, introduces subtle bugs, and produces logic that looks correct but isn't. You end up checking everything yourself, fixing issues manually, and the speed advantage disappears.

The issue isn’t speed. It’s control.

Without a clear way to define and verify behaviour, fast code generation just creates more work. This is where a structured, spec-driven workflow becomes useful.

In practice, this means starting from behaviour, not implementation. You need to define what the system should do using concrete examples, turn those into acceptance tests, then use TDD to build the code in small, verified steps. AI helps at every stage — exploring requirements, suggesting tests, generating code — but the specifications and tests define what "correct" means.  You make AI work within that structure, not around it.


WHAT YOU WILL BUILD

You'll build a production-ready cashback rewards API in Spring Boot from start to finish. But Spring Boot is the vehicle, not the destination. What you're really building is a repeatable, spec-driven workflow for AI-assisted development that you can take to any Java project.

Each section adds a new feature on top of the last. The course currently covers requirements discovery, Claude Code configuration, hexagonal architecture, acceptance testing, TDD with AI, and the complete implementation of the first feature. Upcoming sections apply the same workflow to progressively harder features — category-based cashback rates, monthly caps, refund handling, and redemption — where the real complexity emerges.

The course uses Claude Code, which supports this style of workflow particularly well. You'll learn to use custom commands, hooks, and agents to create a development environment where AI works within your architectural and testing constraints, not around them.

The domain is intentionally realistic. It includes rules around transaction eligibility, category-based cashback rates, monthly caps, refunds, and redemption. As these rules interact, they introduce the kind of edge cases and subtle behaviour you'd expect in a real system. The focus isn't just implementing features — it's managing that complexity in a controlled way, using specifications and tests to keep the behaviour clear while AI helps you move quickly.


WHAT YOU WILL LEARN

By the end of the course, you’ll have a practical way to use AI in day-to-day development without losing control of the code.

You’ll learn how to:

    Structure features as executable specifications before writing any code

    Follow a repeatable cycle for each feature: clarify the behaviour, specify it as tests, build the implementation with AI, then verify the result

    Use AI to explore requirements, generate tests, and implement code, keeping it aligned with the specifications

    Set up Claude Code with custom commands, hooks, and project conventions so it produces code that meets your standards

    Catch the subtle mistakes AI makes before they reach production

A bonus section covers integrating BDD with Cucumber and Gherkin into the AI workflow, including how to guide Claude to produce good-quality Gherkin and avoid common anti-patterns.

You’ll also develop a better sense of where AI is reliable, where it isn’t, and how to catch issues early.


WHAT'S COMING NEXT

The following sections are in production and will be added over the coming weeks:

    Implementing features with more complex business rules

    More advanced Claude topics such as Hooks, Skills and Agents


WHO IS THIS FOR?

This course is aimed at Java developers who use AI coding tools — or are about to — and want a disciplined, reliable approach to AI-assisted development. Some familiarity with Spring Boot will help since it's used as the example project, but the workflow and principles apply to any Java project. The key requirement is an interest in writing clean, well-tested code.


WHO IS THIS NOT FOR?

If you want AI to generate an entire application while you sit back and watch, this isn’t the course for you. This course is for developers who want to stay in control of what they build.

It is also not designed for complete beginners to Java or Spring Boot. The examples use Java 21 and Spring Boot, and the focus is on using specifications, tests and Claude Code to build reliable features — not on teaching Java or Spring Boot from scratch.


OUTCOME

You'll leave with a repeatable workflow you can apply to your next feature, your next project, and every AI interaction after that — along with a clearer sense of where AI is reliable, where it isn't, and how to catch issues early.

Spec-Driven Development(SDD) with AI

Spec-Driven Development(SDD) with AI

Spec-Driven Development(SDD) with AI 
- Learn to use Spec-driven development as a source of truth for building APIs, parallel teamwork, and managing changes

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What you'll learn

  •     Differentiate Spec-Driven Development from vibe coding
  •     Build REST API using AI-assisted Spec-Driven Development
  •     Use AI to create OpenAPI, Requirements, Technical Design, and Tasks.
  •     Learn how to use Kiro IDE for Specification-Driven Development
  •     Learn about the tools for Spec-Driven Development

Agentic AI Development with Agent Framework, MCP and .NET

Agentic AI Development with Agent Framework, MCP and .NET

Agentic AI Development with Agent Framework, MCP and .NET
 - Develop Enterprise Multi-Agent systems using Microsoft Agent Framework, Microsoft Foundry, MCP, Aspire, AG-UI and DevUI

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Learn Python FastApi ( RestAPI & WebSocket & gRPC ) MongoDB

Learn Python FastApi ( RestAPI & WebSocket & gRPC ) MongoDB

Learn Python FastApi ( RestAPI & WebSocket & gRPC ) MongoDB
 - Build a social media backend with FastAPI, MongoDB, JWT, WebSockets, gRPC and Microservices Real-Time Chat, Notification

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The new Udemy course "Learn Python FastAPI (RestAPI & WebSocket & gRPC) MongoDB" offers a project-based, 9-hour curriculum designed to build a scalable, production-style social media backend from scratch. Participants will develop practical skills in FastAPI, MongoDB, JWT authentication, real-time WebSockets, and microservices (gRPC) to create a portfolio-ready social media application. You can explore the course details and curriculum on the Udemy platform.

PMP® Exam Prep 2026: PMBOK® 8 Guide & July 2026 Exam Changes

PMP® Exam Prep 2026: PMBOK® 8 Guide & July 2026 Exam Changes

PMP® Exam Prep 2026: PMBOK® 8 Guide & July 2026 Exam Changes
 - Complete PMP® Exam Preparation Based on the July 2026 Exam Content Outline and PMBOK® 8 Guide

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The PMP® Exam Prep 2026 course is updated for the July 2026 Exam Content Outline, aligning with PMBOK® Guide 8 principles to cover People, Process, and Business Environment domains. Emphasizing practical application over memorization, the curriculum includes AI in project management, Agile/Hybrid approaches, and scenario-based strategies to build the necessary PMI mindset.Get ready for the latest PMP® exam changes and accelerate your project management career.