Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Building a RAG-Powered Q&A Bot is a project-driven course that ends with a working system: ask a question in natural language and get an answer grounded in your own documents, with sources. You will learn the full retrieval-augmented-generation pipeline — cleaning and chunking documents, generating embeddings, storing them in a vector database, retrieving the most relevant context, and prompting an LLM to answer from it. Along the way you will tackle the details that decide quality: chunk size, retrieval strategy, prompt design and guarding against hallucination. You leave with a deployable bot and the understanding to adapt it. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This project course builds a Retrieval-Augmented Generation (RAG) question-answering bot end to end: chunking, embeddings, a vector store, retrieval and an LLM answer layer.
📋 Course Objectives
1. Chunk and embed documents for semantic retrieval.
2. Store and query vectors in a vector database.
3. Assemble a retrieval-augmented-generation pipeline.
4. Design prompts that ground answers and reduce hallucination.
5. Evaluate and tune retrieval and answer quality.
👥 Who Should Enroll?
• Developers building LLM and generative-AI applications
• Data scientists adding retrieval to language models
• Product teams prototyping internal knowledge assistants
• Engineers exploring practical GenAI
🚀 Key Learning Outcomes
• A working RAG Q&A bot over your own document set.
• A reusable retrieval-augmented-generation pipeline.
• The skills to build grounded LLM applications.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Building A Ragpowered Q&A Bot Foundations
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Data Engineering, Preprocessing, and Feature Pipelines
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Model Architecture, Algorithm Design, and Building A Ragpowered Q&A Bot Methods
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Training, Hyperparameter Optimization, and Evaluation
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Deployment, MLOps, and Production Workflows
Implement Building with Education for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects. • Design Hands with Course for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects. • Analyze Building with Education for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects.
Ethics, Bias Mitigation, and Responsible AI Practices
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Industry Integration, Business Applications, and Case Studies
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Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
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