OPEN to: CorporateS, Trainers, faculty Members and students

Agentic AI -RAG based Gen AI Applications

The RAG-Based Generative AI Application Development course is designed for professionals, faculty, and Students looking to build advanced AI-powered applications using Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs).

This program provides a deep dive into integrating LLMs with external knowledge sources, enabling the development of intelligent systems such as enterprise chatbots, knowledge assistants, document search engines, and AI copilots.

Participants will gain hands-on experience in building end-to-end AI applications using real-world datasets, vector databases, embeddings, APIs, and modern AI frameworks. The course emphasizes practical implementation, scalability, and industry use cases to prepare learners for real-world AI development.

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Curriculum

Curriculum covers 80% hands-on session 20% concept explanation.

  • Module 1: Introduction to Generative AI, Agentic AI and LLM

    Theory:
    – Introduction to AI, ML, and Generative AI
    – Evolution of LLMs and AI Agents
    – Industry use cases of Agentic AI
    – How LLMs work (Transformers basics)
    – Tokenization and embeddings
    – Open-source vs proprietary LLMs
    Lab:
    – Setting up Python & AI development environment
    – Introduction to Jupyter Notebook & APIs
    – Working with LLM APIs
    – Basic text generation using LLM

  • Module 3: Vector Databases & Semantic Search, Building RAG Pipeline

    Theory:
    – Vector embeddings and similarity search
    – Introduction to vector databases (FAISS / ChromaDB)
    – RAG workflow architecture
    – Context retrieval and response generation
    Lab:
    – Implementing semantic search system
    – Storing and retrieving embeddings
    – End-to-end RAG pipeline implementation
    – Connecting LLM with vector database

  • Module 5: Capstone Project Development (Implementation Day), & Presentation

    Theory:
    – Project architecture planning
    – Industry deployment considerations
    – AI model deployment basics
    – Ethical AI & Responsible AI practices
    – Future of Agentic AI in industry
    Lab:
    – Dataset preparation (PDF/Docs/Web data)
    – RAG-based AI assistant development
    – UI integration (Streamlit / Web Interface)
    – Final project testing and optimization
    – Project deployment (local/cloud demo)
    – Student presentations & evaluation

  • Module 2: Prompt Engineering (Core for Agentic AI) and RAG

    Theory:
    – Prompt design techniques
    – Zero-shot, Few-shot, Chain-of-Thought prompting
    – Prompt optimization strategies
    – What is RAG architecture
    – RAG vs Fine-tuning
    – Components of RAG pipeline
    Lab:
    – Designing prompts for different tasks
    – Building a prompt-based mini AI assistant
    – Document loading and preprocessing
    – Creating embeddings from text data

  • Module 4: Introduction to AI Agents & Frameworks, Agentic AI with Tools & Memory

    Theory:
    – What are AI Agents?
    – Agent workflows and decision-making
    – Tools and frameworks (LangChain / LlamaIndex overview)
    – Multi-step reasoning in agents
    – Memory in AI agents
    – Autonomous task execution
    Lab:
    – Creating a simple AI agent
    – Tool integration with LLM
    – Building agent with memory and tools
    – Multi-step query handling

Capstone Projects

Project Title: AI-Powered Knowledge Assistant using Agentic RAG

Problem Statement:
Organizations and institutions struggle to manage large volumes of documents and provide instant, accurate responses. A smart AI assistant is needed that can retrieve information from documents and respond intelligently.

Project Description:
Students will build an AI Agent that:
– Reads PDFs/documents / datasets
– Stores embeddings in vector database
– Retrieves relevant context using RAG
-Generates accurate responses using LLM
– Acts as an intelligent knowledge assistant

Industry Relevance:
– Enterprise AI Assistants
– Customer Support Bots
– Academic Research Assistants
– HR & Policy Chatbots

capstone project electrocloudlabs

Unleash the Power

Outcomes

Upon successful completion of this course, participants will gain the ability to design, develop, and deploy RAG-based Generative AI applications using modern tools, frameworks, and real-world datasets. The program ensures a strong balance between conceptual understanding and hands-on implementation, enabling learners to apply their skills in real industry scenarios.

faculty development program electrocloud labs hyderabad

Outcomes help you achieve your milestone!

Strong Understanding of RAG and LLM Architectures

Participants will develop a comprehensive understanding of Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and how these components work together to build intelligent, context-aware AI systems.

Ability to Build End-to-End AI Applications

Learners will gain the capability to design and develop complete AI applications, including data ingestion, embedding generation, retrieval pipelines, and response generation using LLMs.

Expertise in Vector Databases and Semantic Search

Participants will learn how to implement vector databases, embeddings, and semantic search techniques to improve the accuracy and relevance of AI-generated outputs.

Enhanced Productivity and Problem-Solving Skills

Leverage AI tools and frameworks to automate workflows, improve efficiency, and enhance decision-making capabilities across various domains.

Pre-requisites

This course is designed for working professionals, Faculty Members and Students with a basic technical background. The following are recommended:

Required
– Basic understanding of programming concepts
– Familiarity with Python (preferred)
– Basic knowledge of APIs and web technologies
– Understanding of data handling and logic building

Optional (Good to Have)
– Exposure to Machine Learning or AI concepts
– Basic understanding of cloud platforms
– Familiarity with databases or data structures

Technical Setup
– Laptop with internet connectivity
– Ability to install development tools and libraries (guidance will be provided)

Key Features

Our program is designed to deliver a comprehensive and practical learning experience through hands-on training, expert-led sessions, and industry-relevant content. Participants gain exposure to real-world applications, work on live projects, and receive certification that enhances their professional profile.

  • Hands-on RAG Application Development

Build real-world applications using RAG architecture, embeddings, and vector databases.

  • Industry-Focused Curriculum

Learn technologies and frameworks aligned with current industry practices in Generative AI.

  • Capstone Project and Certification

Develop a real-time industry-level project showcasing your ability to build RAG-based AI solution and certification.

  • Expert-Led Training

Learn from industry professionals and subject matter experts with deep domain knowledge and real-world experience.

  • Real-World Use Cases

Work on applications like enterprise chatbots, document intelligence systems, and AI assistants.

  • End-to-End Development Approach

Understand the complete pipeline from data ingestion to deployment of AI applications

How to Get Started with us?

We follow a simple and structured process to help institutions seamlessly plan and organize workshops, FDPs, and training programs on their campus.

roadmap

Explore

Start by exploring our website to understand our program offerings, expertise, and technology domains.
You can review our workshops, faculty development programs, certifications, and industry-focused training initiatives.

Consult

Connect with our experts for a detailed consultation.
During this stage, we help you understand:

– Program structure and learning outcomes
– Technology options and relevance
– Duration, delivery modes, and customization possibilities

Understand & Customize

Based on your institution’s goals, student profile, and departmental requirements, our team will:

– Analyze your specific needs
– Recommend the most suitable program(s)
– Customize the curriculum, duration, and delivery format

Organize

Once finalized, you can proceed to organize the program at your campus by:

– Confirming dates and schedule
– Finalizing participant details
– Coordinating logistics with our team

We take care of end-to-end program delivery, including trainers, content, hands-on sessions, and certification.

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