A Comprehensive Comparison of Prompt Engineering, Finetuning and RAG

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Fine-tuning as a supervised learning process ensures that the model understands and generates content that is highly relevant to a particular task. For example, when I fine-tuned language models used for sentiment analysis, their accuracy improved significantly. Whereas RAG with MyScale provides models with a broader knowledge base, enabling them to generate more contextualized and accurate responses, it faces challenges related to the quality and relevance of the retrieved information.