Unlocking the Potential of Large Language Models
summary
Demonstrates Retrieval-Augmented Generation that cuts hallucinations by fetching relevant passages, using LangChain to orchestrate embeddings into a vector store and returning top five results into a prompt. Claims embeddings are costly so start small, uses Gemini Pro for generation, and shows a prototype (ebook → chunking → embeddings → query) whose poor chunking breaks the demo — a concrete lesson in how chunk size, indexing, and embedding cost determine whether RAG actually works.
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chapters (12)
- 0:00RAG and George RR Martin's problem
- 1:48What is retrieval augmented generation
- 3:57Limitations of large language models
- 7:29How RAG retrieves info
- 9:18Embeddings in RAG
- 12:47Basic RAG workflow
- 14:12Audience use case brainstorm
- 17:53RAG challenges and evaluation
- 19:25Future of RAG efficiency
- 20:10RAG development tools
- 22:57Live RAG demo
- 26:12Closing and Q&A
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