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The Seven Major Challenges Faced by RAG Technology

- Missing Content: Solutions include data cleaning and prompt engineering to ensure the quality of input data and guide the model to answer questions more accurately.

- Unrecognized Top Ranking: This can be resolved by adjusting retrieval parameters and optimizing document ranking to ensure the most relevant information is presented to the user.

- Insufficient Background: Expanding the scope of processing and adjusting retrieval strategies is crucial to include a broader range of relevant information.

- Incorrect Formatting: This can be achieved by improving prompts, using output parsers, and Pydantic parsers, which help to obtain information in the format expected by the user.

- Incomplete Parts: Query transformation can resolve this issue, ensuring a comprehensive understanding of the question and providing a response.

- Unextracted Parts: Data cleaning, message compression, and LongContextReorder are effective strategies for addressing this challenge.

- Incorrect Specificity: This can be solved by more refined retrieval strategies such as Auto Merging Retriever, metadata replacement, and other techniques to further improve the precision of information retrieval.

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