GenAI Toolkits for Developers (LangChain, RAG, Vector DBs)

Use libraries and frameworks to develop powerful AI-powered apps.

Pillar

Technology – Platforms, Tools, Infrastructure & Productivity

Overview

This course introduces developers to cutting-edge Generative AI toolkits such as LangChain, Retrieval-Augmented Generation (RAG), and vector databases. Participants will learn how to leverage these frameworks to build scalable, efficient, and intelligent AI applications that integrate language models with external knowledge sources for enhanced performance.

Learning Objectives

Participants will be able to:

• Understand the capabilities and architecture of LangChain, RAG, and vector databases

• Build AI applications that combine language models with dynamic data retrieval

• Implement vector search to enhance GenAI responses with contextual information

• Develop workflows integrating multiple AI components using modular toolkits

• Optimize performance and scalability of AI-powered applications

Target Audience

• AI and software developers

• Data engineers and machine learning practitioners

• Technical architects and solution designers

• Innovation and R&D teams in AI

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Instructor-led workshops with coding demonstrations

• Hands-on labs to build real-world AI apps

• Group discussions on best practices and architecture patterns

• Q&A sessions and code review feedback

Materials Provided

• Sample code repositories for LangChain, RAG, and vector DB integration

• API documentation and developer guides

• Templates for building modular AI applications

• Slide decks and supplementary reading materials

Outcomes

• Develop AI applications that intelligently integrate external data with LLMs

• Implement vector search for semantic retrieval in GenAI projects

• Use LangChain and similar toolkits to simplify complex AI workflows

• Design scalable and maintainable AI-powered software solutions

Outline / Content

Day 1: Introduction to GenAI Toolkits

• Overview of LangChain, RAG, and vector databases

• Use cases and benefits for AI development

• Setting up development environments and tools

Day 2: Building Applications with LangChain

• Creating modular chains and agents

• Integrating external APIs and data sources

• Hands-on coding: first LangChain application

Day 3: Retrieval-Augmented Generation (RAG) Techniques

• Principles of retrieval-augmented generation

• Implementing RAG workflows for improved responses

• Working with vector embeddings and similarity search

Day 4: Vector Databases and Application Optimization

• Setting up and querying vector databases (e.g., Pinecone, FAISS)

• Optimizing AI workflows for performance and scalability

• Testing, debugging, and deploying GenAI applications