All Topics
💬LLM & GenAI
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RAG Systems

Build Retrieval-Augmented Generation systems to ground LLMs with external knowledge

Recommended for:🤖LLM Engineer🚀Full-Stack AI

Prerequisites

→Python for ML→Prompt Engineering
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Beginner

Beginner

RAG fundamentals

What to Learn

  • •Why RAG? Limitations of parametric knowledge
  • •Document chunking strategies
  • •Embedding models for retrieval
  • •Vector databases (Pinecone, Chroma, FAISS)
  • •Basic RAG pipeline implementation

Resources

  • 📚LangChain RAG tutorials
  • 📚LlamaIndex documentation
  • 📚Pinecone learning center
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Intermediate

Intermediate

Production RAG systems

What to Learn

  • •Hybrid search (semantic + keyword)
  • •Reranking retrieved documents
  • •Query transformation techniques
  • •Evaluation metrics for RAG
  • •Handling multi-modal content

Resources

  • 📚RAGAS evaluation framework
  • 📚Advanced RAG patterns blog posts
  • 📚Cohere reranking documentation
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Advanced

Advanced

Advanced RAG research

What to Learn

  • •Adaptive retrieval strategies
  • •Self-RAG and corrective RAG
  • •Graph RAG and knowledge graphs
  • •Long-context vs RAG tradeoffs
  • •Fine-tuning for retrieval

Resources

  • 📚Self-RAG and CRAG papers
  • 📚GraphRAG paper
  • 📚RAPTOR and long-context RAG papers