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Complete RAG Bootcamp: Build, Optimize, and Deploy AI Apps

Name: Complete RAG Bootcamp: Build, Optimize, and Deploy AI Apps
Rating: 4.1 (48 reviews)

Learn to build intelligent, retrieval-powered AI systems using LangChain, LlamaIndex, and real-world RAG workflows

Created byData Science Academy, School of AI

Last updated 10/2025

English

English [Auto],

What you'll learn

Design and Build a Retrieval-Augmented Generation (RAG) System Understand how to integrate large language models (LLMs) with retrieval pipelines
Implement Embeddings and Vector Databases for Semantic Search Learn how to generate and store embeddings using tools like OpenAI, ChromaDB, or Pinecone
Develop an End-to-End AI Knowledge Assistant Build and deploy a functional AI chatbot using frameworks like LangChain, Streamlit, and FastAPI
Evaluate and Optimize AI Performance Metrics Measure your assistant’s accuracy, relevance, and user experience using key performance metrics

Course content

9 sections • 34 lectures • 6h 31m total length

1.1 What is RAG?9:46
Explore how retrieval augmented generation grounds AI responses in data by retrieving from a knowledge base and generating fact-based answers, while Rag stays current through external knowledge rather than retraining.
1.2 RAG System Overview8:05
Explore the Rag system overview by detailing how the retriever, knowledge base, and generator collaborate to retrieve, read, and generate grounded, up-to-date answers using vector embeddings.
1.3 Applications of RAG10:04
Explore how retrieval augmented generation, or rag, enables real-time, context-aware AI across healthcare, finance, law, education, and customer support by querying live data and generating accurate, up-to-date answers.
Hands on Lab1:50

2.1 The Retrieval Process8:58
Understand the retrieval process in a Rag system, from a query converted to a vector representation to knowledge index searches, and how high-quality retrieval yields accurate, grounded responses.
2.2 The Generation Process8:41
Explore how the generation process translates retrieved knowledge into human-like, grounded responses using large language models; learn about prompts, context, citations, and validation to boost accuracy and trust.
2.3 Putting It Together12:13
Master the end-to-end retrieval augmented generation pipeline—where the retriever supplies context from a knowledge base and the generator delivers fluent, factual, and trusted answers.
Hands on Lab1:48

3.1 What Are Embeddings?11:51
Learn how embeddings turn text into numerical vectors that capture meaning in a semantic space. See how word and sentence embeddings power semantic search, retrieval, and augmented generation.
3.2 Introduction to Vector Databases12:29
Explore how vector databases store embeddings and perform semantic similarity search to enable retrieval augmented generation, using tools like Pinecone, Milvus, and Chroma.
3.3 Building and Querying Vector Stores11:23
Build and query vector stores to turn unstructured data into a memory layer for AI, using embeddings, metadata, and similarity search for grounded, scalable rag.
Hands on Lab2:20

4.1 LangChain Core Concepts11:20
Explore Lang Chain, the modular framework that orchestrates llms, memory, tools, and retrievers to enable retrieval augmented generation, dynamic agents, and context-aware reasoning.
4.2 RAG Implementation with LangChain10:32
Build a rag system by cleaning data, chunking documents, and creating embeddings stored in a vector database; retrieve relevant chunks and generate grounded answers with LangChain.
4.3 Adding Context and Metadata9:52
Learn how context and metadata transform retrieval augmented generation into proactive knowledge engines. Attach metadata during ingestion, inject context into prompts, and evaluate relevance for accurate, grounded responses.
Hands on Lab2:14

5.1 Advanced Retrieval Techniques10:17
Explore advanced retrieval techniques powering RAG systems, from sparse keyword methods to dense embeddings, hybrid retrieval, BI encoders and cross encoders, LLM-based reranking, query expansion, and multi-hop retrieval.
5.2 Optimizing Context and Prompts10:52
Learn to optimize retrieval augmented generation by managing the context window and relevant snippets, chunking text into embeddings, and crafting prompts and chains that ground and refine answers.
5.3 Evaluation and Metrics12:00
Evaluate a retrieval augmented generation system across relevance, factual accuracy, coherence, and efficiency to ensure trustworthy outputs. Use precision, recall, faithfulness, and automated and human evaluation to guide continuous improvement.
Hands on Lab2:28

6.1 Front-End Integration14:06
Design front end integration for retrieval augmented generation, linking user input to Rag backends and delivering real-time, context-rich answers. Use React or Streamlit to enable feedback and transparent context display.
6.2 Backend APIs13:52
Backend APIs act as the nervous system of Rag, linking the user interface to the retriever and generator, coordinating retrieval, generation, and scalable, transparent responses.
6.3 Deployment & Scalability13:54
Explore how to deploy a rag model from prototype to real-time production, choosing architectures (monolithic, microservices, serverless), containerization, CI/CD, monitoring, scaling, and versioning.
Hands on Lab2:35

7.1 Hybrid Search (Keyword + Vector)14:44
Merge keyword and vector search to deliver precise, contextually rich results in RAG pipelines, balancing lexical precision with semantic meaning through fusion, weighting, and normalization.
7.2 Multi-Modal RAG23:14
Explore multi-modal retrieval augmented generation that blends text and images for richer, grounded insights. Discover the four-stage ingestion, embedding, retrieval, generation workflow and unified embedding, primary grounding, or separate stores.
7.3 Agentic RAG22:53
Unleash Agentic Rag by combining retrieval, reasoning, and action in autonomous AI agents. Plan, reflect, use tools, and collaborate across multiple agents to deliver real-time, context-aware insights.
Hands on Lab2:52

8.1 Enterprise & Industry RAG Solutions22:40
Discover how Rag, or retrieval augmented generation, enables context aware, current enterprise insights by retrieving verified internal data and generating precise, explainable decisions across legal, healthcare, and knowledge domains.
8.2 Security & Governance23:21
Secure rag deployments by enforcing access controls, end-to-end encryption, data minimization, and robust governance, aligning with GDPR, HIPAA, and SOC 2 to ensure trustworthy, compliant AI.
8.3 Integrating RAG into Workflows22:09
Learn to integrate retrieval augmented generation (rag) into enterprise workflows, unifying notion, slack, and google drive with a vector database to deliver context-aware real-time answers and insights.
Hands on Lab2:36

9.1 RAG for Developers & Data Scientists20:43
Explore retrieval augmented generation, connecting large language models to your data for context-aware, up-to-date answers, with modular pipelines, vector databases, and frameworks like LangChain and Llama Index.
9.2 Capstone Project22:46
Design and build an AI knowledge assistant using embeddings, vector databases, and retrieval augmented generation to deliver context-aware, accurate answers from your data, with domain selection and end-to-end implementation.

Requirements

Basic Python Programming Skills Familiarity with Python syntax and libraries (like pandas, requests, or json) will make it easier to follow along with code demonstrations.
Curiosity About AI and LLMs A foundational understanding of how Large Language Models (LLMs) like ChatGPT or Llama work conceptually will be helpful, but not mandatory — everything is explained in simple terms.
Access to a Computer with Internet You’ll need a computer capable of running Python and Jupyter notebooks or VS Code, plus an internet connection to install packages and access APIs.
Free or Trial Accounts for Tools Some hands-on labs will use free-tier APIs or tools such as OpenAI, LangChain, ChromaDB, and Streamlit — setup instructions are provided in the course.

Description

“This course contains the use of artificial intelligence”

Unlock the full potential of Retrieval-Augmented Generation (RAG) — the framework behind today’s most accurate, data-aware AI systems.
This comprehensive bootcamp takes you from the fundamentals of RAG architecture to enterprise-level deployment, combining theory, hands-on projects, and real-world use cases.

You’ll learn how to build powerful AI applications that go beyond simple chatbots — integrating vector databases, document retrievers, and large language models (LLMs) to deliver factual, explainable, and context-grounded responses.

What You’ll Learn

The core concepts of Retrieval-Augmented Generation (RAG) and why it’s transforming AI.
Building RAG pipelines from scratch using LangChain, LlamaIndex, and FAISS.
Implementing hybrid search (keyword + vector) for smarter retrieval.
Creating multi-modal RAG systems that process text, images, and PDFs.
Building Agentic RAG workflows where intelligent agents plan, retrieve, and reason autonomously.
Optimizing RAG performance with prompt tuning, top-k selection, and similarity thresholds.
Adding security, compliance, and role-based governance to enterprise RAG pipelines.
Integrating RAG into real-world workflows like Slack, Power BI, and Notion.
Deploying complete front-end and back-end RAG systems using Streamlit and FastAPI.
Designing evaluation metrics (semantic similarity, precision, recall) to measure retrieval quality.

Tools and Technologies Covered

LangChain, LlamaIndex, FAISS, OpenAI API, CLIP, Sentence Transformers
Streamlit, FastAPI, Pandas, Slack SDK, Power BI Integration
Python, LLM Prompt Engineering, and Enterprise Security Frameworks

Real-World Hands-On Labs

Each section of the course includes interactive labs and Jupyter notebooks covering:

RAG Foundations – Build your first retrieval + generation pipeline.
LangChain Integration – Connect document loaders, vector stores, and LLMs.
Performance Optimization – Hybrid, MMR, and context tuning.
Deployment – Launch full RAG applications via Streamlit & FastAPI.
Enterprise Use Cases – Finance, Healthcare, Aviation, and Legal systems.

Who This Course Is For

Developers and Data Scientists exploring AI application design.
Machine Learning Engineers building context-aware LLMs.
Tech professionals aiming to integrate retrieval-augmented AI into products.
Students and researchers eager to understand modern AI architectures like RAG.

Outcome

By the end of this course, you’ll confidently design, implement, and deploy end-to-end RAG systems — combining the power of LLMs with enterprise data for smarter, explainable, and production-ready AI applications.

Who this course is for:

Developers and Programmers who want to integrate Large Language Models (LLMs) with real-time data, APIs, and enterprise workflows.
Data Scientists and Machine Learning Enthusiasts looking to master embeddings, vector databases, and semantic search for practical AI deployment.
AI/ML Students and Researchers eager to build a complete RAG-based knowledge assistant project to strengthen their portfolio or academic work.
Educators and Knowledge Managers interested in automating information retrieval, FAQs, and content summarization within organizations
Entrepreneurs and Innovators aiming to create AI assistants for business domains — from healthcare to finance, support, or education.

Complete RAG Bootcamp: Build, Optimize, and Deploy AI Apps

What you'll learn

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Course content

Introduction to Retrieval-Augmented Generation4 lectures • 30min

Foundations of RAG Architecture4 lectures • 32min

Working with Embeddings and Vector Databases4 lectures • 38min

Section 4: Building RAG Pipelines with LangChain4 lectures • 34min

Enhancing RAG Performance4 lectures • 36min

Deploying RAG Systems4 lectures • 44min

Advanced & Hybrid RAG Techniques4 lectures • 1hr 4min

Real-World Use Cases4 lectures • 1hr 11min

Section 92 lectures • 43min

Requirements

Description

Who this course is for: