RAG & Agentic AI Development & Internship Program

RAG & AGENTIC AI DEVELOPMENT

Home

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

MODULE 1: ADVANCED RAG ARCHITECTURES & VECTOR ENGINEERING

✓

The Vector Stack & Embedding Pipelines

Chunking strategies (semantic vs. recursive), overlap optimization, and embedding model selection
Tools: Pinecone, Milvus, Weaviate
Project: Build a high-density vector store for a technical documentation library (10,000+ pages)

✓

Advanced Retrieval Patterns

Parent document retrieval, multi-query retrieval, and contextual compression
Focus: Hybrid search (keyword + semantic) for high accuracy
Library: LangChain / LlamaIndex

✓

Evaluation & Guardrails (The "Debug" Phase)

Using RAGAS for hallucination detection and faithfulness metrics
Task: Implement a self-correction layer that re-triggers retrieval if context is insufficient

MODULE 2: AGENTIC REASONING & TOOL ORCHESTRATION

✓

Agentic Frameworks: Reasoning Patterns

Implementing ReAct (Reason + Act), Plan-and-Execute, and Reflexion patterns
Focus: Function calling and JSON-mode reliability
Project: Build a SQL agent that converts natural language into complex database queries and executes them safely

✓

Multi-Agent Orchestration

Building agent "crews" with specialized roles like researcher, coder, and reviewer
Tools: CrewAI, AutoGen, LangGraph
Project: Autonomous content agency where multiple agents collaborate on research, coding, and validation

✓

Long-Term Memory & State Management

Implementing working memory vs long-term memory using systems like Redis or PostgreSQL

MODULE 3: PRODUCTIONALIZATION & THE DEV-INTERNSHIP

✓

LLMOps & Monitoring

Tracing agent decisions, latency monitoring, and cost tracking
Tools: LangSmith, Arize Phoenix, Weights & Biases
Internship Task: Optimize a live agent’s token usage and latency by 30% without sacrificing accuracy

✓

Deployment & The Human-in-the-Loop (HITL)

Building approval gates for autonomous actions (e.g., before sending emails)
Tools: FastAPI, Docker, Streamlit for internal tools

✓

Capstone: The Autonomous Enterprise Module

Solve a real-world agency problem such as automating technical support for a SaaS product
Deliverable: A fully deployed multi-agent system with a live monitoring dashboard and reasoning audit logs

TOOLS & LIBRARIES SUMMARY

✓

Tools & Libraries Summary

Category	Tools
Orchestration	LangChain, LlamaIndex, LangGraph
Vector DBs	Pinecone, ChromaDB, Weaviate
Agentic Frameworks	CrewAI, AutoGen, LangGraph
Evaluation	RAGAS, LangSmith, TruLens
Hosting & API	FastAPI, Docker, Vercel AI SDK

What Our Students Say

Man standing outdoors in a green mountainous landscape, wearing a blue sleeveless jacket and jeans.

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.