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Are you already using RAG (= Retrieval Augmented Generation) in your AI Agent to add your corporate documents to your favorite LLM? Or do you plan to set up an enterprise team using RAG and Generative AI? We have been using RAG architectures since last summer. In the past year, we discovered several improved solutions when using Langchain and RAG. Some to note: 💡 Do not use a normal text parser for processing complex document structures. You need advanced parsing mechanisms to avoid loss of information. 💡 Use a vector DB or some vector data structure to query your embeddings. 💡 Enforce language settings, so that your supervisor and child processes speak the same language. 💡 Use a well-defined JSON schema to enforce API compatibility across LLMs (system prompt might not be enough - thanks to Juri Wiens and Mark Breen for pointing this out). What are you currently tackling with RAG or planning to? Tell me your opinion in the comments and we will send you our reference architecture + explanations into your LinkedIn mailbox. #generativeai #langchain #niologic #aivillage #NRWkannKI