Learn how to use Amazon SageMaker Studio to build a RAG question answering solution with Llama 2, LangChain, and Pinecone for fast experimentation. Implementation involves using notebooks, developing the solution, deploying the models, improving the answer with context, implementing RAG question answering with embeddings and
Table of contents
Using notebooks for RAG-based question answeringSolution overviewPrerequisitesSet up the notebook and environmentLoad the pre-trained model and tokenizerAsk a question that requires up-to-date informationImprove the answer by adding context to the promptImplement RAG question answering with BGE embeddings and PineconeClean upConclusionSort: