Building RAG Pipelines with LLMs
Building RAG Pipelines with LLMs is a Intermediate-level, 3 Weeks online program by NSTC. Master Retrieval‑Augmented Generation, Large Language Models, Vector Search through hands‑on projects, real datasets, and expert mentorship.
- ModeOnline (e-LMS)
- LevelIntermediate
- Duration3 Weeks
- Fee₹5,999
| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹5999 / $99 |
| Tools | OpenAI GPT Cohere Claude Llama Hugging Face FAISS Weaviate Pinecone Qdrant LangChain |
About the Building RAG Pipelines with LLMs 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.
RAG grounds AI responses in factual external data, a must‑learn skill for developers, researchers, and innovators in legal tech, finance, healthcare, and education.
Program Highlights
• Comprehensive coverage of Building RAG Pipelines with LLMs from fundamentals to advanced applications
• Hands-on projects and real-world case studies in AI
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: OpenAI GPT, Cohere, Claude, Llama
• Career-oriented training for academic and professional growth in AI
Course Curriculum
Module 1: 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
Module 2: 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
Module 3: 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
