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.
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.