Retrieval Augmented Generation (RAG)

December 1, 2025 Courses
Retrieval Augmented Generation (RAG)

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 49 Lessons (4h 24m) | 1.36 GB

Retrieval Augmented Generation (RAG) improves large language model (LLM) responses by retrieving relevant data from knowledge bases—often private, recent, or domain-specific—and using it to generate more accurate, grounded answers.

In this course, you’ll learn how to build RAG systems that connect LLMs to external data sources. You’ll explore core components like retrievers, vector databases, and language models, and apply key techniques at both the component and system level. Through hands-on work with real production tools, you’ll gain the skills to design, refine, and evaluate reliable RAG pipelines—and adapt to new methods as the field advances.

Across five modules, you’ll complete hands-on programming assignments that guide you through building each core part of a RAG system, from simple prototypes to production-ready components.

What you’ll learn

  • How to design and build RAG systems tailored to real-world needs
  • How to weigh tradeoffs between cost, speed, and quality to choose the right techniques for each component of a RAG system
  • A foundational framework to adapt RAG systems as new tools and methods emerge

Skills you’ll gain

  • Prompt Engineering
  • Application Security
  • Large Language Modeling
  • Natural Language Processing
  • Artificial Intelligence
  • Semantic Web
  • LLM Application
  • Generative AI
  • System Monitoring
  • ChatGPT

Through hands-on labs, you’ll

  • Build your first RAG system by writing retrieval and prompt augmentation functions and passing structured input into an LLM.
  • Implement and compare retrieval methods like semantic search, BM25, and Reciprocal Rank Fusion to see how each impacts LLM responses.
  • Scale your RAG system using Weaviate and a real news dataset—chunking, indexing, and retrieving documents with a vector database.
  • Develop a domain-specific chatbot for a fictional clothing store that answers FAQs and provides product suggestions based on a custom dataset.
  • Improve chatbot reliability by handling real-world challenges like dynamic pricing and logging user interactions for monitoring and debugging.
  • Develop a domain-specific chatbot using open-source LLMs hosted by Together AI for a fictional clothing store that answers FAQs and provides product suggestions based on a custom dataset.

You’ll apply your skills using real-world data from domains like media, healthcare, and e-commerce. By the end of the course, you’ll combine everything you’ve learned to implement a fully functional, more advanced RAG system tailored to your project’s needs.

Table of Contents

rag-overview

rag-overview
1 a-conversation-with-andrew-ng
2 module-1-introduction
3 introduction-to-rag
4 applications-of-rag
5 rag-architecture-overview
6 introduction-to-llms
7 introduction-to-information-retrieval
8 join-the-deeplearning-ai-forum-to-ask-questions-get-support-or-share-amazing_instructions

graded-assignments
9 optional-downloading-your-notebook-and-refreshing-your-workspace_instructions

module-1-wrap-up
10 module-1-conclusion
11 lecture-notes-m1_instructions

information-retrieval-and-search-foundations

information-retrieval-and-search-foundations
12 module-2-introduction
13 retriever-architecture-overview
14 metadata-filtering
15 keyword-search-tf-idf
16 keyword-search-bm25
17 semantic-search-introduction
18 semantic-search-embedding-model-deepdive
19 hybrid-search
20 evaluating-retrieval

module-2-wrap-up
21 module-2-conclusion

information-retrieval-with-vector-databases

information-retrieval-with-vector-databases
22 module-3-introduction
23 approximate-nearest-neighbors-algorithms-ann
24 vector-databases
25 chunking
26 advanced-chunking-techniques
27 query-parsing
28 cross-encoders-and-colbert
29 reranking

module-3-wrap-up
30 module-3-conclusion

llms-and-text-generation

llms-and-text-generation
31 module-4-introduction
32 transformer-architecture
33 llm-sampling-strategies
34 choosing-your-llm
35 prompt-engineering-building-your-augmented-prompt
36 prompt-engineering-advanced-techniques
37 handling-hallucinations
38 evaluating-your-llm-s-performance
39 agentic-rag
40 rag-vs-fine-tuning

module-4-wrap-up
41 module-4-conclusion

rag-systems-in-production

rag-systems-in-production
42 module-5-introduction
43 what-makes-production-challenging
44 implementing-rag-evaluation-strategies
45 logging-monitoring-and-observability
46 customized-evaluation
47 quantization
48 cost-vs-response-quality
49 latency-vs-response-quality
50 security
51 multimodal-rag

module-5-wrap-up
52 module-5-conclusion

Homepage

Resolve the captcha to access the links!