Programme Parameters
Graduate / Intermediate
3 Weeks (30 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 RAG Pipelines with LLMs is a specialized, project‑based program that teaches you how to combine the power of Large Language Models (OpenAI GPT, Cohere, Claude, Llama) with custom knowledge sources through Retrieval‑Augmented Generation. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Building RAG Pipelines with LLMs is a specialized, project‑based program that teaches you how to combine the power of Large Language Models (OpenAI GPT, Cohere, Claude, Llama) with custom knowledge sources through Retrieval‑Augmented Generation.
📋 Course Objectives
1. Apply Artificial Intelligence methods to authentic research and industry problems.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in Artificial Intelligence
• R&D engineers and working professionals applying Artificial Intelligence in industry
• Academics and educators building research or teaching capacity in Artificial Intelligence
🚀 Key Learning Outcomes
• A portfolio-grade Artificial Intelligence deliverable you can defend and extend.
• 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
Week 1 – Foundations of Retrieval‑Augmented Generation
Explore what Retrieval‑Augmented Generation is and why it matters • Identify core components of a RAG pipeline • Analyze benefits and limitations of RAG architectures
Week 2 – Building the Core RAG Stack
Generate embeddings using OpenAI and Hugging Face models • Apply chunking and preprocessing strategies for optimal retrieval • Design prompt templates tailored for RAG workflows • Connect LLMs to chosen vector databases
Week 3 – Optimization, Deployment & Capstone
Implement hybrid search (BM25 + embeddings) for superior recall • Extend RAG to structured and unstructured data sources • Create multi‑turn conversational RAG experiences
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | OpenAI GPT |
| Covered Tool / Platform | Cohere |
| Covered Tool / Platform | Claude |
| Covered Tool / Platform | Llama |
| Covered Tool / Platform | Hugging Face |
| Covered Tool / Platform | FAISS |
| Covered Tool / Platform | Weaviate |
| Covered Tool / Platform | Pinecone |
| Covered Tool / Platform | Qdrant |
| Covered Tool / Platform | LangChain |
Frequently Asked Questions
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