Course Overview
Course Distinction
What makes our course unique?
What makes our course unique?
RAG is not a trend — it is the standard architecture for grounded, trustworthy AI in enterprises. Banks, hospitals, law firms, and technology companies are all deploying RAG. This course teaches you to build it.
End-to-End Pipeline Mastery
You will not learn RAG in isolation. You will build the complete pipeline from document upload to LLM response—understanding every component deeply enough to debug, optimize, and scale it in production.
Multiple Vector Databases Covered
You will gain hands-on experience with ChromaDB, Pinecone, Weaviate, and FAISS—so you can choose the right vector database for any project requirement, from local development to cloud-scale enterprise deployment.
Production-Grade, Not Just Proof of Concept
Most RAG tutorials build demos. This course builds production-ready systems — with API layers, error handling, monitoring, security, and cloud deployment that make RAG usable in real enterprise environments.
Course Content
- Why LLMs hallucinate and how RAG solves it
- The RAG pipeline — retrieve, augment, generate
- RAG vs fine-tuning — when to use each approach
- Overview of RAG components and system design
- What embeddings are and how they represent meaning
- Embedding models — OpenAI, Hugging Face, Sentence Transformers
- Cosine similarity and nearest neighbor search
- Evaluating embedding quality for your domain
- ChromaDB — local vector storage for rapid development
- Pinecone — managed, scalable cloud vector search
- Weaviate — open-source vector DB with filtering and hybrid search
- FAISS — Facebook AI Similarity Search for high-performance retrieval
- Choosing the right vector database for your use case
- Document loaders — PDF, DOCX, HTML, databases, and APIs
- Chunking strategies — fixed-size, recursive, and semantic chunking
- Metadata tagging for filtered and structured retrieval
- Handling complex documents — tables, images, and mixed content
- LangChain document loaders and text splitters
- Vector store integration and retrieval chains
- Naive RAG — the foundational retrieval pipeline
- LCEL (LangChain Expression Language) for composable pipelines
- Conversational RAG with memory and chat history
- HyDE — Hypothetical Document Embeddings for improved recall
- Query expansion and multi-query retrieval
- Re-ranking with cross-encoder models
- Hybrid search — combining dense and sparse retrieval (BM25)
- Contextual compression — reducing noise in retrieved chunks
- OpenAI ChatCompletion API for RAG response generation
- Claude API for long-document analysis and reasoning
- Prompt templates for grounded, source-cited responses
- Hallucination detection and confidence scoring
- Streaming responses for production applications
- Building a RAG API with FastAPI
- Containerisation with Docker
- Deploying to AWS, GCP, and Azure
- Monitoring retrieval quality and response accuracy
- Security, authentication, and cost optimisation
Key Features
-
✔ Instructor-led interactive sessions -
✔ Four vector database hands-on labs -
✔ Real enterprise document datasets -
✔ Industry-recognized certification -
✔ Full LangChain pipeline code provided -
✔ Capstone deployment on cloud
Skills Covered
- RAG Architecture & System Design
- Text Embeddings & Semantic Search
- ChromaDB, Pinecone, Weaviate & FAISS
- LangChain RAG Chains & LCEL
- Document Processing & Chunking Strategies
- Advanced RAG — HyDE, Re-Ranking, Hybrid Search
- OpenAI & Claude API Integration
- FastAPI for RAG Application Development
- Cloud Deployment of RAG Systems
- Hallucination Reduction & Response Quality
Advancements
- RAG System Developer
- AI/ML Engineer
- LLM Application Engineer
- AI Solutions Architect
- Knowledge Base AI Developer
- Enterprise AI Engineer
Business Impact
FAQ?
Customized Corporate Training
We also provide corporate training programs tailored for organizations looking to implement AI automation and Agentic AI solutions.
Custom curriculum
Industry-specific use cases
Flexible training delivery
Enterprise consulting support
Who Should Attend
Python Developers
AI/ML Engineers
Data Engineers
Software Developers
Solutions Architects
Tech Leads
Data Scientists
Enterprise AI Teams
Stop Letting Your Knowledge Sit in Files. Put It to Work.
Master Retrieval-Augmented Generation and build AI systems that answer from your data — accurately, reliably, and at scale.
✔ Learn from industry experts
✔ Work on real enterprise datasets
✔ Earn certification
Testimonial
What people are say?
















