Challenges and Future Directions
Despite its advantages, RAG faces several challenges:
- Complexity: Combining retrieval and generation adds complexity to the model, requiring careful tuning and optimization to ensure both components work seamlessly together.
- Latency: The retrieval step can introduce latency, making it challenging to deploy RAG models in real-time applications.
- Quality of Retrieval: The overall performance of RAG heavily depends on the quality of the retrieved documents. Poor retrieval can lead to suboptimal generation, undermining the model’s effectiveness.
- Bias and Fairness: Like other AI models, RAG can inherit biases present in the training data or retrieved documents, necessitating ongoing efforts to ensure fairness and mitigate biases.
What is Retrieval-Augmented Generation (RAG) ?
RAG, or retrieval-augmented generation, is a new way to understand and create language. It combines two kinds of models. First, retrieve relevant information. Second, generate text from that information. By using both together, RAG does an amazing job. Each model’s strengths make up for the other’s weaknesses. So RAG stands out as a groundbreaking method in natural language processing.
What is Retrieval-Augmented Generation (RAG) ?
Table of Content
- What is Retrieval-Augmented Generation (RAG)?
- The Basics of Retrieval-Augmented Generation (RAG)
- Significance of RAG
- What problems does RAG solve?
- Benefits of Retrieval-Augmented Generation (RAG)
- Challenges and Future Directions
- RAG Applications with Examples
- Advanced Question-Answering System
- Content Creation and Summarization
- Conversational Agents and Chatbots
- Information Retrieval
- Educational Tools and Resources
- Example Scenario: AI Chatbot for Medical Information
- Retrieval-Augmented Generation (RAG)- FAQs
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