13 Oct Disadvantages of Retrieval Augmented Generation (RAG)
RAG includes the retrieval and generation process, and this can be complex and includes significant resources. Let us see some of the disadvantages of RAG:
- Complexity: The combination of retrieval and generation phases adds complexity to the model architecture and training process.
- Computationally Resources are high: Requires significant computational resources for both retrieval and generation, especially when dealing with large datasets.
- Dependence on quality of documents and data: The quality of the generated responses heavily depends on the quality and relevance of the retrieved documents.
- Latency in the retrieval phase: The retrieval step can introduce latency, making it challenging to provide real-time responses in some applications.
- Evaluation Challenges: Evaluating the quality and relevance of generated responses can be subjective and difficult to quantify.
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Read More:
- What is Machine Learning
- What is a Machine Learning Model
- Types of Machine Learning
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- Feedforward Neural Networks (FNN)
- Convolutional Neural Network (CNN)
- Recurrent Neural Networks (RNN)
- Long short-term memory (LSTM)
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