RAG Basics
Build a basic RAG pipeline — chunk, embed, retrieve, and generate.
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After this skill you can…
- Chunk documents with LangChain or LlamaIndex
- Generate embeddings and store in a vector DB
- Build a Q&A app over your own data
Prerequisites
Watch (10 videos)
LangChain Tutorial for Beginners: Prompt Templates and Placeholders
→ Build RAG pipelines→ Implement table-safe chunking→ Use Qdrant for hybrid retrieval
Two Limits No Model Has Escaped
→ Implement RAG pipelines→ Use vector search for LLMs
What exactly is retrieval augmented generation?
→ Implement RAG in a language model→ Use vector databases for information retrieval→ Enhance LLMs with external knowledge
RAG Explained Visually 🤯 | The AI Technique Behind ChatGPT's Smart Answers
→ Understand RAG basics→ Implement RAG in AI applications→ Improve question answering accuracy
When All Context Matters: Extended Cache Augmented Generation - Luis Romero-Sevilla, Orbis
→ Implement Extended Cache Augmented Generation→ Use GraphRAG for knowledge graph construction→ Optimize retrieval methods for dynamic datasets
HyDE Retrieval in Python: Improve RAG Recall with SentenceTransformers
→ Improve RAG recall→ Generate hypothetical answers
What Is a VECTOR DATABASE? Simple Explanation
→ Understand vector databases→ Implement semantic search→ Apply AI embedding models
Google RAG Secret to Higher Rankings w/ Josh Bachynski #shorts
→ Understand RAG technology→ Apply RAG for improved search rankings
8. Redis Implementation | Explained in Tamil | RAG | AI Agents | GenAI | LLM | VectorDB | Caching
→ Implement RAG search→ Use semantic caching with Redis
What is Vector Database? #rag #generativeai #llms
→ Understand vector databases→ Use embedding models for data conversion→ Apply vector databases for LLM model context
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