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This research article explores the process of building an LLM (Language Model-based Learning) application using document loaders, embeddings, vector stores, and prompt templates. LLMs have become increasingly popular in natural language processing tasks due to their ability to generate coherent and contextually relevant text. This article discusses the importance of LLMs, compares fine-tuning and context injection approaches, introduces LangChain, and provides a step-by-step process for building the LLM app. Python code snippets are included where applicable.
