A Comprehensive Comparison of Prompt Engineering, Finetuning and RAG(myscale.com)
myscale.com
A Comprehensive Comparison of Prompt Engineering, Finetuning and RAG
https://myscale.com/blog/prompt-engineering-vs-finetuning-vs-rag/
4 comments
As I'm exploring using a vector database for my CBT mental health counselor chatbot, this article's insights on the strengths of RAG and the potential benefits of integrating with a solution like MyScale are quite compelling. How could such an integration further enhance the conversational abilities and personalization of a counseling-focused AI assistant?
Thank you for your interest and question. Simply put, you may often get unrealistic answers when trying out chatbots, i.e. the "hallucination of a large language model". The integration of RAG and MyScale effectively solves this hallucination and improves the accuracy of answers. For a concrete example, please refer to this blog:Teach your LLM to Always Answer with Facts not Fiction（https://myscale.com/blog/teach-your-llm-vector-sql/）. If you want to customize your personalized solution, please contact us（https://myscale.com/contact/） and we will give you the best price and the best quality.
in what situations would finetuning a model still be preferable over using RAG with MyScale?
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.