A production-focused AI engineering course for backend developers covering AI foundations, prompt engineering, RAG pipelines, AI agents, agentic RAG, AI system architecture, and prototype-to-productio
Included with Pro, Enterprise, or One-time payment
Loved by learners at thousands of companies
AI Engineering is a comprehensive, production-focused course designed to take backend engineers from foundational AI concepts to building, deploying, and optimizing real AI-powered systems. This is not a surface-level AI overview — it is a structured engineering curriculum that teaches you how to design, build, and ship AI systems that belong in production codebases.
The course begins with <
Master AI Engineering from fundamentals to advanced concepts
Build real-world projects applying what you've learned
Understand best practices and industry standards
Gain confidence to work on production-level applications
Prepare for technical interviews and career advancement
This chapter provides an introduction to the AI Engineering course, outlining the full learning journey, what you will build, and how each module connects to production-grade AI system development.
This chapter builds your foundational understanding of modern AI systems. You'll learn how text generation, multimodal AI, structured outputs, function calling, the Model Context Protocol, and reasoning models work — giving you the engineering mental model required to build on top of these systems e …
This chapter provides a comprehensive, production-grade guide to prompt engineering. You'll progress from basic prompting techniques through advanced patterns including chain-of-thought reasoning, few-shot prompting, prompt chaining, routing and orchestration, reflection, and prompt optimization — a …
This chapter covers Retrieval-Augmented Generation (RAG) from the ground up. You'll learn embeddings, vector stores, chunking strategies, and how to build complete RAG pipelines — including agentic RAG patterns. The chapter includes practical walkthroughs and hands-on tasks to solidify your understa …
This chapter provides a deep dive into AI agents — autonomous systems that can reason, plan, use tools, and take actions. You'll learn the ReAct pattern, tool integration, agent loops, memory systems, workflow orchestration, error handling, and human-in-the-loop patterns, culminating in building pra …
This chapter is a real-world project module where you'll see a complete AI-powered Legal Case Workflow system designed, built, and reviewed. You'll understand how AI agents, RAG, and prompt engineering come together to solve a real business problem end to end.
This advanced chapter covers Agentic RAG — combining autonomous AI agents with retrieval-augmented generation to build intelligent systems that can classify intent, route queries, interact with databases safely, and handle complex data retrieval workflows. You'll build a Text2SQL agent and explore p …
This chapter covers the architectural thinking required to design AI systems at scale. You'll explore AI system architecture patterns, learn about coding agents, build one hands-on, and take on a challenge working with Claude Code to deepen your understanding of agentic coding tools.
This final chapter bridges the gap between AI prototypes and production-grade systems. You'll learn how to evaluate AI systems using DeepEval, implement advanced RAG system patterns for reliability, and apply cost optimization and caching strategies to run AI systems efficiently at scale.
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"I truly appreciate the high-quality material in this course. The structured lessons, hands-on projects, and clear explanations make learning a great experience. I look forward to future additions and updates! Thanks for your polite and friendly attitude."
"The practical examples and hands-on exercises were particularly beneficial. They not only reinforced the theoretical concepts but also allowed me to apply them in real-world scenarios. The inclusion of best practices and common pitfalls added a practical dimension to the learning process."
"The course is an excellent resource for beginners. Your explanations of the basics are clear, making it easy for newcomers to grasp. I particularly enjoyed the task management application; it's a practical example that helps solidify the concepts."
Some programming experience is recommended. Check the course description for specific prerequisites.
This course has 5 hours of content. Complete it at your own pace.
Yes! Upon completion you'll receive a certificate you can share on LinkedIn and your resume.
Yes, all code examples and resources are available for offline access.
Yes — career guidance, resume reviews, and mock interviews are available through the platform.