RAG and Agentic AI Professional Certificate

Master the art of building intelligent AI applications using Retrieval-Augmented Generation (RAG) and agentic AI frameworks. This 8-course professional certificate covers everything from GenAI fundamentals to advanced multi-agent systems.

Program Overview

This professional certificate program consists of 8 comprehensive courses designed to take you from GenAI basics to production-ready agentic AI applications.

01. Develop GenAI Apps

Generative AI Fundamentals

Natural Language Processing

LangChain Basics

Prompt Engineering

Building First GenAI App

02. Build RAG Applications

RAG Architecture

Document Processing

Retrieval Strategies

Context Integration

RAG Implementation

03. Vector Databases for RAG

Vector Embeddings

Database Selection

ChromaDB & Pinecone

Similarity Search

Performance Optimization

04. Advanced RAG Techniques

Multi-Query RAG

Hybrid Search

Re-ranking Methods

Context Compression

RAG Evaluation

05. Multimodal GenAI Apps

Vision & Language Models

Image-Text Integration

Audio Processing

Video Understanding

Cross-Modal Applications

06. Fundamentals of AI Agents

Agent Architecture

Tool Integration

Memory Systems

Agent Decision Making

Basic Agent Patterns

07. Agentic AI with LangChain & LangGraph

LangChain Agents

LangGraph Fundamentals

State Management

Multi-Agent Systems

Complex Workflows

08. Agentic AI Frameworks

Framework Comparison

AutoGen & CrewAI

Agent Deployment

Production Patterns

Capstone Project

Learning Path

The courses build progressively:

1. Foundation (Courses 1-2): Start with GenAI basics and RAG fundamentals
2. Technical Deep Dive (Courses 3-5): Master vector databases, advanced RAG, and multimodal AI
3. Agentic Systems (Courses 6-8): Build autonomous agents and multi-agent systems

Key Technologies

Throughout this certificate, you’ll work with:

• LangChain & LangGraph - Agent orchestration frameworks
• Vector Databases - ChromaDB, Pinecone, Weaviate
• LLM APIs - OpenAI, Anthropic, IBM watsonx
• RAG Frameworks - LlamaIndex, Haystack
• Agentic Frameworks - AutoGen, CrewAI

Course Details

Browse the courses below to begin your learning journey:

In this section

• Develop Generative AI Applications Get Started
  This course provides an introduction to developing generative AI applications using modern tools and frameworks. It covers prompt engineering, LangChain fundamentals, and building your first GenAI applications with practical hands-on projects.
  • Module-1

    Foundation of GenAI and Prompt Engineering

    • Generative AI
      This document introduces generative AI, its evolution from discriminative AI and the foundational models that enable creative content generation across text, images, video, and code.
    • Foundation Models
      This document explores foundation models and large language models, covering their training methodology, advantages in performance and productivity, as well as challenges related to compute costs and trustworthiness in enterprise applications.
    • Natural Language Processing
      This document introduces natural language processing, explaining how computers translate between unstructured human language and structured data through techniques like tokenization, stemming, lemmatization, part of speech tagging and named entity recognition.
    • Guide to Gen Ai
      This document provides a comprehensive reference guide to generative AI covering fundamental concepts, key models, applications, and best practices for implementing GenAI solutions.
    • In-Context Learning
      This document introduces in-context learning and prompt engineering explaining how LLMs can learn new tasks from examples provided in prompts without additional training, along with techniques for crafting effective prompts to guide AI systems.
    • LangChain
      This document introduces LangChain, an open-source Python framework for developing LLM applications, exploring its benefits, practical uses, and integration capabilities with various data types.
    • Advanced Methods of Prompt Engineering
      This document explores advanced prompt engineering methods including zero-shot, few-shot, chain-of-thought, and self-consistency techniques, along with practical tools and applications for effective LLM interactions.
    • LangChain Expression Language
      This document introduces LangChain Expression Language (LCEL), covering how to build flexible chains using the pipe operator, structure prompts with templates, and develop reusable patterns for AI applications.
  • Module-2
    • LangChain Core Concepts
      This document defines LangChain and explores its core components including language models, chat models, chat messages, prompt templates, example selectors, and output parsers for building LLM applications.
    • LangChain Chains and Agents for Budilding LLM Applications
      This document describes chains in LangChain for generating responses, memory storage mechanisms, and agents for dynamic action sequencing to build sophisticated LLM applications.
    • LCEL Chaining Method
      This document describes how to build flexible, composable chains using LangChain Expression Language (LCEL), including prompt templates, pipe operators, runnable primitives, and type coercion mechanisms.
  • Module-3
    • Choose the Right AI Models for Use Case
      This document explores the multi-model approach for AI implementation covering model selection criteria, prompt engineering, continuous evaluation and collaborative team strategies for optimal AI deployment.
    • Building Apps with Generative AI
      This document covers the complete AI application development journey, from ideation and model selection through building with RAG and fine-tuning to production deployment with MLOps best practices.
    • Flask Web Framework
      This document introduces Flask, a Python micro web framework, covering its main features, installation process, built-in dependencies, popular community extensions, and key differences from Django.
    • Python with Flask for Large-Scale Projects
      This document explores Flask's capabilities for large-scale web development covering extensibility, scaling considerations, modular development patterns real-world enterprise applications, and HTTP status code handling for production deployments.