Part 01: https://t.me/AIMLDeepThaught/959
Part 02: https://t.me/AIMLDeepThaught/967
Part 03: https://t.me/AIMLDeepThaught/975
Part 04: https://t.me/AIMLDeepThaught/979
1. Introduction
01. Overcome the limitations of LLMs with RAG
02. Limitations of LLMs
03. Use cases for retrieval-augmented generation RAG
2. Getting Started
01. Using GitHub Codespaces
02. Setting up your environment
03. Choosing an LLM and embeddings provider
04. Setting up LLM accounts
05. Choosing a vector database
06. Setting up a Qdrant account
07. Downloading our data
3. Fundamental Concepts in LlamaIndex
01. How LlamaIndex is organized
02. Using LLMs
03. Loading data
04. Indexing
05. Storing and retrieving
06. Querying
07. Agents
Hands-On AI_ RAG using LlamaIndex Part 02
4. Introduction to RAG
01. Components of a RAG system
02. Ingestion pipeline
03. Query pipeline
04. Prompt engineering for RAG
05. Data preparation for RAG
06. Putting it all together
07. Drawbacks of Naive RAG
5. RAG Evaluation
01. Introduction to RAG evaluation
02. Evaluation metrics
03. How to create an evaluation set
Hands-On AI_ RAG using LlamaIndex - Part 03
5. Advanced RAG Pre-Retrieval and Indexing Techniques
01. How we can improve on Naive RAG
02. Optimizing chunk size
03. Small to big retrieval
04. Semantic chunking
05. Metadata extraction
06. Document summary index
07. Query transformation
6. Advanced RAG Post-Retrieval and Other Techniques
01. Node post-processing
02. Re-ranking
03. FLARE
04. Prompt compression
05. Self-correcting
Hands-On AI_ RAG using LlamaIndex - Part 04
7. Modular RAG
01. Hybrid retrieval
02. Agentic RAG
03. Ensemble retrieval
04. Ensemble query engine
8. Conclusion
01. LlamaIndex evaluation
02. Comparative analysis of retrieval-augmented generation technique
