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How to Make Your LLM More Accurate with RAG & Fine-Tuning
- Large Language Models (LLMs) like ChatGPT can be enhanced with RAG and fine-tuning for improved accuracy and customization.
- RAG enables LLMs to access external knowledge sources during inference without changing internal weights, allowing for up-to-date information retrieval.
- On the other hand, fine-tuning involves training LLMs with specific data to internalize domain-specific knowledge, enhancing task-specific performance.
- RAG is useful for dynamic data retrieval and reducing computational requirements, while fine-tuning tailors LLMs for specific industries or companies.
- Combining RAG and fine-tuning in RAFT offers deep expertise and real-time adaptability by enriching LLMs with domain knowledge and external information.
- Both methods have distinct advantages: RAG for dynamic knowledge integration, and fine-tuning for stable, task-specific optimization.
- RAG and fine-tuning can be used together to extend LLM capabilities and are valuable tools in AI applications, serving complementary purposes.
- RAG requires fewer resources initially, but more during inference, while fine-tuning is resource-intensive during training but efficient in operation.
- The choice between RAG and fine-tuning depends on the level of dynamism in the data and the need for specific task optimization.
- Hybrid approaches like RAFT combine RAG and fine-tuning to leverage both methods' advantages for comprehensive LLM enhancement.
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