What will I learn in the Building RAG Pipelines with LLMs course?

You will learn how to design and develop Retrieval-Augmented Generation pipelines using document processing, text embeddings, vector databases, semantic search, prompt engineering, large language models and response evaluation techniques.

What is Retrieval-Augmented Generation?

Retrieval-Augmented Generation is an AI architecture that retrieves relevant information from an external knowledge source and provides it to a large language model so the model can generate more accurate, contextual and grounded responses.

Who should enroll in this RAG and LLM course?

The course is suitable for artificial intelligence learners, software developers, data scientists, machine learning engineers, researchers, technical professionals and anyone interested in building practical generative AI applications.

What is the level and duration of the course?

Building RAG Pipelines with LLMs is an intermediate-level online program delivered over three weeks.

Does the course cover vector databases and embeddings?

Yes. The course covers text embeddings, vector representations, vector database implementation, similarity search and the retrieval of relevant document chunks for large language model applications.

Will I build a real-world RAG pipeline?

Yes. The program focuses on practical RAG development, including document ingestion, chunking, embedding generation, vector storage, information retrieval, prompt construction and grounded response generation.

Why are RAG pipelines useful for LLM applications?

RAG pipelines help large language models use current, domain-specific or private information without retraining the entire model. They can improve response relevance, reduce unsupported outputs and provide better control over the knowledge used by an AI application.

Building RAG Pipelines With LLMs

Attribute

Detail

Format

Online (e-LMS)

Level

Intermediate

Duration

3 Weeks

Certification

e-Certification + e-Marksheet

Tools

OpenAI GPT, Cohere, Claude, Llama, Hugging Face, FAISS

About the Rag Pipelines 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

Tools, Techniques, or Platforms Covered

OpenAI GPT
Cohere
Claude
Llama
Hugging Face
FAISS
Weaviate
Pinecone
Qdrant
LangChain

Real-World Applications

Apply Building RAG Pipelines with LLMs skills directly to academic research, thesis work, and publications
Build a professional portfolio showcasing practical AI competencies
Solve industry-relevant problems using Building RAG Pipelines with LLMs methodologies and tools
Contribute to open-source projects and collaborative research in AI
Prepare for competitive examinations, interviews, and professional certifications in AI

Who Should Attend & Prerequisites

Students pursuing degrees in AI, science, engineering, or related disciplines
Working professionals seeking to upskill or transition into AI roles
Researchers and academicians looking to adopt modern techniques in AI
Entrepreneurs, freelancers, and self-learners interested in practical AI knowledge

Prerequisites: Some familiarity with basic concepts in AI will be helpful but is not mandatory. A willingness to learn and engage with hands-on exercises is essential.

Frequently Asked Questions

1. What is the format of this Building RAG Pipelines with LLMs course?

This is an Online (e-LMS) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

2. Will I receive a certificate after completing this course?

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from NanoSchool (NSTC) that you can showcase on your CV and LinkedIn profile.

3. What are the prerequisites for this course?

Learners should have a foundational understanding of AI concepts. Familiarity with basic tools and programming is recommended.

4. How long will I have access to the course materials?

You will have access to all course materials for the duration of 3 Weeks. The self-paced format allows you to learn according to your own schedule through our online learning management system.

5. Is mentor support available during the course?

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to AI. Our mentors are industry experts and experienced professionals.

Enroll in Building RAG Pipelines with LLMs today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering AI skills that matter.

Brand	NSTC
Format	Online (e-LMS)
Duration	3 Weeks
Level	Intermediate
Domain	AI
Hands-On	Yes – Practical projects with industrial datasets
Tools Used	OpenAI GPT, Cohere, Claude, Llama, Hugging Face, FAISS, Weaviate, Pinecone, Qdrant, LangChain, LlamaIndex

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