CF

The Applied AI Masterclass

20h 52m 37s
English
Paid

The Applied AI Masterclass is a 6-lesson 20 hours 52 minutes self-paced course by Arpit Bhayani. Intensive on Implementing Artificial Intelligence is a practice-oriented program for engineers, developers, and those who want to confidently create modern AI applications based on LLM, AI agents, and intelligent workflow systems.

Course facts

Lessons
6
Duration
20 hours 52 minutes
Level
All levels
Language
English
Updated
Instructor
Arpit Bhayani
Price
Premium

Intensive on Implementing Artificial Intelligence is a practice-oriented program for engineers, developers, and those who want to confidently create modern AI applications based on LLM, AI agents, and intelligent workflow systems. The course helps you progress from advanced prompting to building full-fledged production-ready solutions that include memory, tools, retrieval modules, multi-agent architectures, and quality assessment systems.

What the Program Includes

The intensive is built around real practice: you will encounter 24 working prototypes and 6 full projects that will help you learn not just to use models but to create reliable AI systems for products and business processes. Development is conducted in Python, and the studied approaches remain model-agnostic—skills applicable to Gemini, OpenAI, Anthropic, and other LLMs.

What You Will Learn

  • Work with LLM and apply modern prompting techniques.
  • Use Few-shot, Chain-of-Thought, structured outputs, and methods for reducing model errors.
  • Apply Tool Calling and build production-grade RAG systems.
  • Design AI agent architecture and agent-based workflows.
  • Create multi-agent systems and establish effective memory management.
  • Conduct quality assessments of AI systems, ensure security, and protect models from prompt injection.
  • Scale AI products: caching, fallback mechanisms, streaming, observability.

Practice and Projects

Each week includes two in-depth practical sessions and an independent project. Among them:

  • Fact Checking Agent
  • Coding Agent
  • Multi-agent Code Reviewer
  • Incident Auto-Remediation Agent
  • Natural Language Workflow Engine

Who the Course is For

The program is designed for developers who are proficient in Python and want to move from experimenting with models to creating reliable AI systems ready for production. Participants will need their own API access to Gemini, OpenAI, or Anthropic—the course does not provide LLM credits for development and testing.

What You Will Get After Completion

You will retain lifetime access to all session recordings, as well as receive an extensive library of practical AI patterns that can be used in your own products, startups, and engineering teams.

Who teaches The Applied AI Masterclass? Arpit Bhayani

Arpit Bhayani thumbnail

Arpit Bhayani is a US-based software engineer (formerly at Amazon, Practo, and now an independent educator) who runs asliengineering.com and the Arpit Bhayani YouTube channel, both focused on system design and database internals. His material is unusually deep for the system-design-interview market — taking serious detours into the actual implementation of Redis, Postgres, and the data structures behind them.

His CourseFlix listing carries three Arpit Bhayani courses: The System Design Masterclass, Redis Internals (an end-to-end study of how Redis is implemented in C), and System Design for Beginners. Material is paid and aimed at engineers preparing for system-design interviews or doing infrastructure work on production data systems.

What lessons are included in The Applied AI Masterclass?

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#1: 001 Prompt LLMs Reliably
All Course Lessons (6)
#Lesson TitleDurationAccess
1
001 Prompt LLMs Reliably Demo
03:53:07
2
002 Tool Use and RAG in Production
03:32:38
3
003 Building Agents That Work
03:09:48
4
004 Multi-Agent Systems and Memory
03:52:47
5
005 Evaluation, Safety, and Agentic Systems
03:14:10
6
006 Productionization and Gotchas
03:10:07
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Books

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Frequently asked questions

What prerequisites are needed before enrolling in this course?
Prospective students should have a solid foundation in programming, especially in Python, as the course involves developing AI systems using this language. Familiarity with basic machine learning concepts and experience in working with large language models (LLMs) would be beneficial, but not strictly necessary, as the course will cover modern prompting techniques and the use of AI agents.
What projects will I work on during the course?
The course includes 24 working prototypes and 6 full projects. These projects are designed to help you apply what you learn in real-world scenarios. For instance, you will work on building production-grade retrieval-augmented generation (RAG) systems and creating multi-agent systems. These projects will allow you to practice building reliable AI systems for products and business processes.
Who is the target audience for this course?
This course is aimed at engineers and developers who are interested in implementing modern AI applications. It is also suitable for those looking to expand their skills in building AI systems with LLMs, AI agents, and intelligent workflow systems. The course is practice-oriented, making it ideal for individuals who want to confidently create production-ready AI solutions.
How does this course compare in depth and scope to similar AI courses?
The Applied AI Masterclass focuses on practical implementation and the development of production-ready AI systems. Unlike some courses that may only cover theoretical aspects or specific models, this course offers hands-on experience with various approaches, including multi-agent architectures, quality assessment systems, and memory management. The skills gained are model-agnostic, applicable to a range of LLMs like Gemini, OpenAI, and Anthropic.
What specific tools and platforms will be covered in the course?
The course will primarily use Python for development, and it will cover various techniques for working with large language models (LLMs). You will learn to apply tool calling, design AI agent architectures, and build production-grade RAG systems. The course also covers caching, fallback mechanisms, streaming, and observability for scaling AI products.
What topics are not covered in this course?
While the course is comprehensive in terms of implementing AI applications, it does not focus on the foundational theories of machine learning or deep learning algorithms. It assumes a certain level of familiarity with these concepts and instead focuses on the application of AI systems in production settings.
How much time should I expect to commit to this course?
The course is intensive and includes two in-depth practical sessions each week, along with independent project work. While the exact runtime is not specified, students should be prepared to dedicate a significant amount of time to both the instructional content and hands-on projects to fully benefit from the learning experience.