Building Generative AI Applications 

Duration: 3 days / 24 hrs

Introduction 

This 3-day hands-on course helps participants design, build, evaluate, and deploy real Generative AI applications using modern LLM frameworks. It goes beyond prompts—covering RAG, agentic workflows, monitoring, and scaling—so teams can move from demos to production-ready GenAI systems. 

Objectives 

By the end of the course, participants will be able to: 

  • Understand how GenAI and LLMs actually work under the hood 
  • Design effective prompts, workflows, and application architectures 
  • Build LLM-powered apps using APIs, LangChain, and LangGraph 
  • Evaluate, deploy, monitor, and scale GenAI applications responsibly 

Key Takeaways 

Participants will leave with: 

  • Practical experience building end-to-end GenAI applications 
  • A clear understanding of RAG, agentic AI, and multi-agent patterns 
  • Confidence to take GenAI solutions from prototype to production 

Training Methodology (Learning by Doing) 

  • Hands-on first: Every concept is reinforced through live coding and guided exercises 
  • Build as you learn: Participants incrementally develop GenAI components across the 3 days 
  • Real scenarios: Use cases, challenges, and debugging tasks mirror real-world problems 
  • Iterate & improve: Continuous evaluation, testing, and refinement—not one-off demos 

Course Outline: 

Understanding Generative AI 

  • Definitions: Intelligence, Artificial Intelligence, Generative AI 
  • Differences between Traditional AI and GenAI 
  • Tokenization as an entry point 
  • Benefits and challenges 
  • Popular GenAI models and frameworks 
  • LLM settings and parameters 
  • Advanced concepts: Prompt Engineering, RAG, Agentic AI 

Prompt Engineering 

  • Importance of prompt engineering 
  • Techniques: 
  • Zero-shot prompting 
  • One-shot and few-shot prompting 
  • Chain-of-Thought and Tree-of-Thoughts 
  • Evaluating and refining prompts 
  • Common mistakes 

Building LLM-Based Applications 

  • Application design building blocks 
  • Accessing LLMs via APIs 
  • Prompt templates 
  • Conversational model of completion 
  • Closed-weight vs. open-weight models 
  • Batch APIs for cost control 

Accelerating Development with LangChain 

  • Overview and terminology 
  • LLM chains and prompt templates 
  • Structured output 
  • Retrieval Augmented Generation (RAG) 
  • Loaders, splitters, and parsers 
  • Function calling 

Evaluating Generative AI Applications 

  • Differences from traditional software evaluation 
  • Key metrics and interpretation 
  • Advanced evaluation techniques 
  • Custom metrics 

Deploying and Scaling GenAI Applications 

  • Unique challenges 
  • Managing latency and throughput 
  • Data privacy and security 
  • Cloud vs. on-premise trade-offs 
  • Continuous deployment pipelines 
  • Monitoring and auto-scaling 

Monitoring GenAI Applications 

  • Evaluation vs. monitoring 
  • Key monitoring metrics 
  • Workflow setup 
  • Alerts, logs, and verification 
  • Monitoring system setup 

Debugging and Testing 

  • Debugging strategies and tools 
  • Testing methodologies 
  • Continuous integration and deployment 

Agentic AI with LangGraph 

  • LangGraph concepts and principles 
  • Common agentic patterns 
  • Multi-agent workflows 
  • Communication and coordination 
  • Error handling 
  • Scaling agentic systems 

Use Cases and Ethics 

  • Real-world applications 
  • Ethical considerations 
  • Responsible use of GenAI 
  • Case studies and examples