Think Different

Integrating and optimizing Large Language Model (LLM) frameworks with various prompting strategies in Python requires careful consideration of the specific libraries and your desired use case.   1. RAG RAG (Retrieval-Augmented Generation) is a technique that uses a retrieval model to retrieve relevant documents from a knowledge base, and then uses a generative model to generate text based on the retrieved documents. To integrate RAG with an LLM framework, you can use the  rag  module in LangChain. This module provides a simple interface for using RAG with different LLMs. To optimize RAG, you can use a variety of techniques, such as: Using a larger knowledge base Using a more powerful retrieval model Using a more powerful generative model Tuning the hyperparameters of the RAG model 2. ReAct Prompting ReAct prompting is a technique that uses prompts to guide the LLM towards generating the desired output. To integrate ReAct prompting with an LLM framework, you...