RAG How To Talk To Your Data
summary
Explains that Retrieval-Augmented Generation pairs LLMs with external sources to cut hallucinations and stay current and shows a working pipeline: Cohere LLM + Mistral embeddings stored in a Pinecone vector DB, LangChain/LangGraph orchestration, LangSmith tracing, 1,000-character chunks, 1,024-dim vectors, top-5 similarity retrieval. Demonstration loads a reinforcement‑learning lecture PDF, upserts embeddings, retrieves instructor names and topical passages, and returns concise answers; notes costs, latency, ambiguity and privacy as the remaining practical challenges.
AI-written summary · about
chapters (13)
- 0:00RAG introduction
- 1:06LLM foundations
- 12:23What is RAG
- 28:44RAG vs fine-tuning
- 32:21LangChain overview
- 35:13Building RAG step-by-step
- 41:48Processing PDFs for RAG
- 51:09Pinecone embeddings
- 58:12Vector database querying
- 1:01:20LangGraph workflow
- 1:14:10RAG challenges
- 1:25:26RAG tools and best practices
- 1:30:13Q&A
related talks
35mBoost LLMs with Event Streaming and Retrieval Augmented Generation
42mTo RAG or not to RAG
53mFrom RAG to Riches: Secrets of Data-Augmented LLMs
48mCrafting your own RAG system: Leveraging 30+ LLMs for enhanced performance
57mLLMs gone wild
28mNavigating Challenges and Enhancing Performance of LLM based Applications
48mGoing Beyond Prompt Engineering
50mRetrieval Augmented Generation Beyond Toy Demos