It seems simple, but this tests one's "skill" in writing prompts.

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The role of prompt is to guide the model to generate a specific type of text. A good prompt can guide the model to generate text in the desired way. For example, if we want the model to write an article about global warming, we can give the model a prompt such as "Global warming is a serious problem because...". The model will generate an article based on this prompt. The advantage of this method is that it is simple and intuitive, but the disadvantage is that it may require a lot of trying to find a good prompt.

The second type of Function calling is a more in-depth application architecture. It directly obtains certain characteristics of the model by calling the internal functions of the model. For example, we can call the model's word vector function Argentina WhatsApp Number to obtain the word vector of a word. The advantage of this method is that it can directly obtain the internal information of the model, but the disadvantage is that it requires an in-depth understanding of the internal structure of the model. The third type of RAGRetrieval-Augmented Generation RAG is an application architecture that combines retrieval and generation.





In this approach, the model first retrieves relevant text and then uses this text as input for the model to generate an answer. For example, if we want the model to answer a question about global warming, the model can first retrieve some articles about global warming and then generate an answer based on these articles. The advantage of this method is that it can utilize a large amount of external information to improve the quality of model generation. But the disadvantage is that it requires a lot of computing resources because a large amount of text needs to be retrieved. The fourth type of fine-tuning is fine-tuning.