Building Generative AI Applications: From Prototype to Production

This course equips developers with the practical skills to integrate Generative AI (GenAI) into real-world applications. From chatbots and content generation to predictive analysis and multi-agent workflows, learners will design, deploy, and scale intelligent systems with confidence. Each module combines hands-on labs, debugging strategies, and case studies to prepare participants for challenges in production environments.

3 Days

Custom

· Software Developers adding GenAI features to applications.

· Data Scientists & Analysts exploring applied AI solutions.

· Python proficiency.

· Familiarity with generative AI concepts and tools.

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

· Enhance customer experiences with LLM-powered features.

· Automate workflows and processes with custom GenAI apps.

· Build modular applications using LangChain and LangGraph.

· Evaluate generative AI outputs using robust metrics.

· Deploy, monitor, and scale GenAI applications securely.

· Design agent-based workflows and ensure responsible AI use.

Course Outline

Module 1: Foundations of Generative AI

· What makes AI “generative”?

· Generative AI vs. traditional AI approaches

· How LLMs process and predict tokens

· Popular GenAI models and frameworks (OpenAI, Anthropic, open-source)

· Benefits, challenges, and limitations in production

· Lab: Explore tokenization and decoding with a Python demo

Module 2: Prompt Engineering & Retrieval-Augmented Generation (RAG)

· Why prompt engineering matters for developers

· Direct prompting: zero-, one-, and few-shot examples

· Advanced prompting: Chain-of-Thought and Tree-of-Thoughts

· Avoiding common pitfalls in prompt design

· Retrieval-Augmented Generation: loaders, splitters, and retrievers

· Lab: Build and refine prompts for a question-answering system

Module 3: Designing LLM-Based Applications

· Key design building blocks for GenAI applications

· Using APIs for LLM access: closed-weight vs. open-weight trade-offs

· Prompt templates and conversational completion models

· Managing performance and cost with batch APIs

· Lab: Create a simple chatbot that integrates external data

Module 4: Accelerating Development with LangChain

· Core LangChain concepts: chains, memory, structured output

· RAG pipelines with LangChain (documents → queries → responses)

· Function calling and external tool integration

· Parsers, splitters, and workflow design

· Lab: Build a document Q&A bot with LangChain

Module 5: Evaluating Generative AI Applications

· Why traditional software metrics aren’t enough

· Core GenAI evaluation metrics: relevance, coherence, factuality

· Advanced evaluation: embedding similarity, human-in-the-loop scoring

· Creating custom evaluation pipelines

· Lab: Evaluate outputs from your chatbot for accuracy and bias

Module 6: Deploying & Scaling GenAI Systems

· Challenges unique to GenAI deployment (latency, cost, hallucinations)

· Cloud vs. on-prem deployment trade-offs

· Continuous deployment pipelines for GenAI

· Monitoring applications: metrics, alerts, and auto-scaling

· Lab: Deploy your chatbot to the cloud and set up basic monitoring

Module 7: Debugging & Testing GenAI Applications

· Debugging unpredictable LLM behavior

· Testing strategies for nondeterministic outputs

· Tools for reproducibility and regression testing

· Integrating CI/CD for GenAI workflows

· Lab: Troubleshoot and optimize your chatbot with logging tools

Module 8: Agentic AI with LangGraph

· Introduction to LangGraph and agent orchestration

· Common agent patterns: planners, executors, evaluators

· Multi-agent workflows: collaboration and coordination

· Error handling and fault tolerance in agent systems

· Lab: Build a multi-agent system that coordinates task execution

Module 9: Use Cases, Ethics & Responsible AI

· Real-world applications across industries: finance, healthcare, retail, and education

· Risks: bias, misuse, data privacy, and intellectual property

· Frameworks for responsible AI development (NIST, EU AI Act, AI Bill of Rights)

· Case Study Workshop: Ethical dilemmas in customer-facing GenAI apps

Module 10: Capstone Project – From Prototype to Production

Participants design and present a complete GenAI application that:

· Uses LangChain or LangGraph for orchestration

· Integrates RAG for external data retrieval

· Has monitoring and evaluation pipelines

· Addresses ethical and security concerns

· Is ready for deployment at scale