RAG & Agentic AI Development & Internship Program

RAG & Agentic AI Development & Internship

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About The Course

The RAG & Agentic AI Development & Internship Program is a beginner-to-intermediate level course designed to help you build real-world AI systems powered by Retrieval-Augmented Generation (RAG) and autonomous AI agents. This program bridges the gap between theory and industry-ready implementation by combining hands-on development, production-grade workflows, and internship-style project experience.

You’ll learn how modern AI systems move beyond simple chatbots to intelligent, tool-using, decision-making agents capable of querying databases, searching documents, calling APIs, and executing multi-step tasks autonomously.

Whether you’re a student, developer, or aspiring AI engineer, this program will help you design, build, deploy, and optimize AI agents used in real businesses today.

Key points

Lessons of the Course

Program Details

1: Introduction to RAG & Agentic AI

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RAG Foundations

What is RAG and why it matters
Limitations of vanilla LLMs
Enhancing LLMs with retrieval systems

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Agentic AI Overview

Real-world use cases of agentic systems
Autonomous decision-making systems
Enterprise AI transformation examples

2: LLM Foundations & Prompt Engineering

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LLM Architecture

How LLMs work (Transformers basics)
Tokenization and attention mechanisms
Training vs inference

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Prompt Engineering

Prompt strategies for accuracy and reliability
System, user, and tool prompts
Few-shot and chain-of-thought prompting

3: Embeddings & Vector Databases

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Text Embeddings

Text embeddings explained
Chunking strategies for long documents
Embedding models overview

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Vector Search

Indexing and semantic search
Vector databases (FAISS, Pinecone, Weaviate)
Similarity search techniques

4: Building Your First RAG Application

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RAG Pipeline

Document ingestion pipelines
Retrieval + generation flow
Improving relevance and responses

5: Agentic AI Fundamentals

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Agent Architecture

What makes an AI “agent”
Planning, reasoning, and execution loops
Tool-calling architecture

6: Tool-Using AI Agents

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External Tool Integration

Connecting APIs, databases, and external tools
File handling & search agents
Action-based workflows

7: Multi-Agent Systems

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Agent Collaboration

Agent collaboration patterns
Task delegation & coordination
Real-world enterprise use cases

8: Memory, Context & Optimization

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Context Management

Short-term vs long-term memory
Reducing hallucinations
Performance tuning strategies

9: Deployment & Production Readiness

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Production Deployment

Backend integration
Security & access control
Cost optimization strategies

10: Internship Project & Final Evaluation

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Capstone & Evaluation

End-to-end AI product development
Capstone project presentation
Code review & feedback

What You’ll Build

AI Project Implementations

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AI Document Assistant (RAG-Based)

Build an AI document assistant using Retrieval-Augmented Generation (RAG)
Upload PDFs, DOCX, or knowledge documents for contextual querying
Semantic search with embeddings and vector database integration
Context-aware responses with reduced hallucinations

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Knowledge-Base Chatbot for Businesses

Custom chatbot trained on company policies and documentation
Internal support automation (HR, IT, Operations)
Secure access control and role-based querying
Deployable on websites or internal dashboards

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Autonomous Research Agent

Agent capable of searching, summarizing, and analyzing online data
Multi-step reasoning and planning loops
Source citation and structured report generation
Automated research workflows

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API-Calling Task Automation Agent

Connect external APIs for real-time data access
Automate repetitive workflows (email, CRM, reporting)
Tool-calling architecture with structured outputs
Error handling and execution monitoring

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Multi-Agent Workflow System

Design collaborative AI agents with task delegation
Coordinator agent + specialized worker agents
Shared memory and communication protocols
Enterprise-grade orchestration workflows

Internship Experience

Guided Internship Experience & Certification

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Real-World AI Experience

Work on practical, industry-relevant AI problems
Apply RAG, agentic systems, and deployment strategies in real scenarios
Develop production-focused thinking and solution design skills

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Portfolio-Ready Projects

Build structured, end-to-end AI applications
Maintain clean GitHub repositories with documentation
Create demonstrable projects for interviews and job applications

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Industry Development Practices

Follow Agile-based task execution and milestone tracking
Implement clean architecture and scalable design patterns
Practice professional Git workflows and structured code reviews

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Mentorship & Continuous Feedback

Collaborate on structured tasks and defined milestones
Receive regular mentor reviews and performance insights
Guided improvement planning for technical growth

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Certification

Internship Completion Certification awarded to successful candidates
Recognition of practical AI development and deployment skills<

Tools & Technologies Used

Core Technologies & Tools

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Programming Languages

Python for AI application development and backend logic
JavaScript for API integrations and front-end connectivity

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LLM & AI APIs

OpenAI APIs and other LLM providers
Prompt engineering and structured tool calling
Token management and response optimization

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RAG Frameworks

LangChain for orchestration and agent workflows
LlamaIndex for document indexing and retrieval pipelines
Modular pipeline architecture for scalable AI systems

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Vector Databases

FAISS for local vector similarity search
Chroma for lightweight embedded vector storage
Pinecone for scalable cloud-based vector indexing

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Backend & Integration

Building REST APIs for AI services
Integration with backend frameworks (Flask, FastAPI, Express)
Connecting databases, external APIs, and AI pipelines

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Cloud & Deployment

Containerization basics with Docker
Deploying AI services to cloud platforms
Environment configuration and production readiness fundamentals

What Our Students Say

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Learning MERN Stack at Excendra was transformative. Practical projects, clear explanations, and industry-focused mentorship helped me develop strong full-stack skills and confidence to build professional applications.

Mehran Muzaffar Software Developer COMTECH GROUP

I completed Full Stack Development training at Excendra. Practical projects and clear teaching built my skills and confidence. With their support, I secured a job soon after the course and recommend Excendra for tech career.

Hurain Javaid Software Engineer Sun-Net Inc.