The Applied AI Masterclass by Arpit Bhayani

The Applied AI Masterclass by Arpit Bhayani is an implementation-first AI engineering program designed for software engineers, backend developers, system architects, and technical leaders who want to move beyond prompt experimentation and learn how to build production-ready AI systems.

Unlike traditional AI courses that focus heavily on machine learning theory, this masterclass focuses entirely on Applied AI Engineering—teaching practical architecture patterns, agentic workflows, Retrieval-Augmented Generation (RAG), evaluation frameworks, AI safety, observability, and scalable system design.

Led by Arpit Bhayani, Principal Engineer at Razorpay and former Staff Engineer at Google, the program helps engineers develop the skills needed to design, implement, and deploy reliable AI products that can operate at scale in real-world environments.

What’s Included

• 6 Live Applied AI Sessions
• 24+ Practical AI Prototypes
• 7 Complete Agentic System Designs
• RAG Engineering Frameworks
• AI Agent Development Systems
• Multi-Agent Architecture Patterns
• AI Evaluation & Safety Frameworks
• Production AI Infrastructure Strategies
• Lifetime Access to Recordings
• Community Access
• Course Pre-Reads & Resources

What You’ll Learn

Inside The Applied AI Masterclass, you’ll learn how to:

• Build production-ready AI applications
• Design reliable LLM workflows
• Implement advanced RAG systems
• Reduce hallucinations in AI outputs
• Build AI agents with memory and tools
• Create multi-agent systems
• Design evaluation pipelines
• Improve AI reliability and safety
• Scale AI infrastructure efficiently
• Deploy fault-tolerant AI products

Course Curriculum

Session 1: Prompt LLMs Reliably

Learn how professional AI systems handle prompting and output generation.

Topics include:

• Chain-of-Thought Prompting
• Few-Shot Prompting
• Structured Outputs
• Prompt Reliability
• Prompt Failure Modes
• LLM Evaluation Strategies

System Design Project

• Fact Checking Agentic System

Session 2: Tool Use & RAG in Production

Master Retrieval-Augmented Generation systems used in modern AI applications.

Topics include:

• Tool Calling
• Tool Schema Design
• Hybrid Search
• Metadata Filtering
• Query Rewriting
• Re-Ranking Systems
• Semantic Caching
• Parallel Tool Execution

System Design Project

• RAG Over 10 Million Documents

Session 3: Building Agents That Work

Understand the architecture behind reliable AI agents.

Topics include:

• ReAct Framework
• Observe → Think → Act Loops
• Planning Systems
• Execution Frameworks
• Checkpoint & Resume
• Human-in-the-Loop Systems
• Agent Context Management

System Design Project

• Paper-to-Code Agent

Session 4: Multi-Agent Systems & Memory

Learn how multiple agents collaborate effectively.

Topics include:

• Memory Architecture
• Working Memory Management
• Memory Compression
• Agent Coordination
• Specialist Agents
• Critic & Refiner Systems
• Mixture of Agents
• Deadlock Resolution

System Design Project

• Incident Auto-Remediation System

Session 5: Evaluation, Safety & Reliability

Build trustworthy AI applications using evaluation frameworks.

Topics include:

• LLM-as-a-Judge
• Human Evaluation Systems
• Adversarial Testing
• Regression Testing
• AI Safety Frameworks
• Performance Measurement

System Design Projects

• AI Code Reviewer
• Self-Updating Documentation Platform

Session 6: Production Architecture & Scale

Learn how enterprise AI systems are deployed and maintained.

Topics include:

• Observability
• Prompt Caching
• Cost Attribution
• Reliability Engineering
• Graceful Degradation
• Production Monitoring
• Scaling AI Infrastructure

System Design Project

• Natural Language Workflow Engine

Why This Course Stands Out

Most AI programs focus on tools and prompt engineering.

The Applied AI Masterclass focuses on:

LLMs → Tools → RAG → Agents → Multi-Agent Systems → Evaluation → Safety → Production Architecture

This approach teaches engineers how to build complete AI products rather than isolated demos.

Key Benefits

• Learn production-grade AI engineering
• Build real-world AI systems
• Master agentic architectures
• Reduce hallucinations and failures
• Design scalable RAG systems
• Improve AI reliability
• Learn enterprise deployment patterns
• Understand AI observability and evaluation
• Build practical engineering intuition
• Stay ahead in Applied AI development

Who This Course Is For

• Software Engineers
• Backend Developers
• Full Stack Engineers
• AI Engineers
• Machine Learning Engineers
• Tech Leads
• Engineering Managers
• Startup Founders
• System Architects
• Developers Building AI Products

About Arpit Bhayani

Arpit Bhayani is a Principal Engineer at Razorpay, former Staff Engineer at Google, and creator of multiple highly regarded engineering programs. With more than a decade of experience building large-scale distributed systems, databases, AI infrastructure, and developer platforms, he focuses on teaching practical engineering skills that can be immediately applied in production environments.

His teaching style combines deep technical concepts, real-world architecture discussions, implementation details, and engineering-first thinking.

Final Thoughts

Arpit Bhayani – The Applied AI Masterclass is one of the most practical AI engineering programs available for developers who want to move beyond theory and learn how modern AI systems are actually built.

From prompt reliability and RAG architectures to agents, multi-agent systems, AI evaluation, safety, and production deployment, this program provides a complete roadmap for engineers who want to design, build, and scale real-world AI applications with confidence.

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