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Unclaimed Profile"Prompt Engineering for Developers: The Definitive Guide" by Lauro Fialho Müller is an essential, highly practical masterclass designed for software engineers, developers, and tech architects aiming to harness the full potential of Large Language Models (LLMs). Rather than focusing on simple conversational prompts, this course dives deep into the engineering principles required to build reliable, scalable, and cost-effective AI-driven applications. Students will master advanced prompting patterns such as Chain of Thought, Persona, and Self-Critique, which are critical for tackling complex reasoning tasks. The curriculum emphasizes real-world software integration, teaching developers how to guarantee machine-readable outputs like JSON, handle function calling for external tool execution, and manage API costs in multi-turn workflows. A standout feature is the hands-on instruction on using LiteLLM to write provider-agnostic code, allowing seamless transitions between OpenAI, Anthropic, and local models. Additionally, learners will explore modern development workflows using AI coding agents like GitHub Copilot and build a complete AI-powered CLI tool from scratch. With a focus on robust software practices, the course also covers unit testing for AI applications by mocking external APIs. Whether you are looking to build smart automations or integrate sophisticated AI agents into existing systems, this course delivers the concrete engineering skills needed to transition from basic prompting to production-grade AI system architecture.
About the creator
Lauro Fialho Müller
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Lauro Fialho Müller is a seasoned software engineer and DevOps specialist dedicated to bridging the gap between traditional system administration and modern, AI-driven development workflows. With years of hands-on experience in scripting, automation pipelines, and cloud infrastructure, Lauro has transitioned into helping developers optimize their day-to-day operations using Python and cutting-edge artificial intelligence tools. His teaching philosophy centers on immediate, practical application, ensuring that students do not just memorize syntax but learn how to engineer real-world solutions. Through his courses, such as his deep dive into Python for DevOps and his comprehensive guide to developer-focused prompt engineering, Lauro equips his…Show more
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Program Overview
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Learning format
Subcategory
Price
Price may change · updated within 1–2 weeks
Course language
What You'll Learn
- Understand core LLM mechanics like tokenization and context windows to build efficient and cost-effective solutions.
- Apply advanced prompt engineering patterns like Persona, Chain of Thought, and Self-Critique to solve complex problems.
- Engineer prompts that ensure reliable, machine-readable output formats like JSON for use in automated systems.
- Implement function calling to allow your AI application to execute external tools like linters and security scanners.
- Calculate and manage API costs for complex, multi-turn AI conversations and workflows.
- Write provider-agnostic code with LiteLLM that can seamlessly switch between OpenAI, Anthropic, and local models.
- Leverage AI coding agents like GitHub Copilot to accelerate your software development workflow from scaffolding to testing.
- Build a complete, AI-powered CLI tool from scratch that generates smart commit messages and performs code reviews.
- Write comprehensive unit tests for AI-integrated code by effectively mocking external API calls and user input.
Best For
- Software engineers looking to integrate LLMs into production-grade applications.
- Developers who need to master function calling and structured data extraction.
- Technical professionals aiming to build provider-agnostic AI tools using LiteLLM.
- Engineers interested in implementing rigorous unit testing for AI-driven software.
Not For
- Absolute beginners who have never written code or used a terminal.
- Non-technical users seeking general advice on how to use ChatGPT for basic tasks.
- Researchers focused exclusively on the theoretical mathematics behind deep learning architectures.
- Individuals looking for a low-code or no-code solution for automation.
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