What You’ll Learn: RAG Fundamentals
You’ll go from understanding LLM limitations to architecting and implementing powerful RAG systems that ground AI responses in specific, retrieved knowledge.
LLM Fundamentals & Prompting
Deep dive into LLM capabilities, limitations, and advanced prompting techniques.
LangChain Framework
Use LangChain for chain orchestration, memory, and connecting different components.
Vector Databases (FAISS)
Index and retrieve documents using FAISS for efficient similarity search.
RAG Pipeline Deployment
Deploy your RAG application using frameworks like FastAPI or Gradio.
Who Is This Course For?
Ideal for experienced AI engineers and developers looking to build knowledge-grounded AI applications.
- ML engineers wanting to specialize in RAG and contextual AI
- Developers building chatbots or search applications powered by LLMs
- Researchers exploring ways to ground LLMs in private data
Hands-On Projects
Corporate Document Q&A Bot
Build a RAG system to answer questions about company handbooks or policies.
Research Paper Assistant
Create a tool to summarize and find information within scientific articles.
Capstone
Custom RAG Application
Design and deploy a RAG pipeline for a domain-specific problem of your choice.
3-Week RAG Syllabus
~36 hours total • Lifetime LMS access • 1:1 mentor support
Week 1: LLMs & LangChain Foundations
- LLM capabilities, limitations, and common failure modes
- Introduction to LangChain: Chains, Agents, Tools
- Basic RAG concept and simple implementation
- Advanced prompting techniques for RAG
Week 2: Embeddings & Vector Stores
- Text embedding models (e.g., Sentence Transformers)
- Document chunking and preprocessing strategies
- Indexing documents with FAISS
- Similarity search and retrieval mechanisms
Week 3: Advanced RAG & Deployment
- Advanced RAG patterns (hybrid search, multi-hop)
- Evaluating RAG pipeline performance
- Building a web interface for your RAG app
- Capstone project: End-to-end RAG system
NSTC‑Accredited Certificate
Share your verified credential on LinkedIn, resumes, and portfolios.
Frequently Asked Questions
Yes, a strong understanding of LLMs (e.g., GPT, Llama), their capabilities, and experience with Python and frameworks like Hugging Face Transformers or OpenAI API is essential. Familiarity with basic NLP concepts is required.
Yes! You will build end-to-end RAG applications, including ingesting documents, creating vector stores, and querying them using an LLM to generate contextual answers.
