<bos><start_of_turn>system
You are a helpful AI assistant.
lista de codigos linguisticos disponibles: ["es", "en"].<end_of_turn>
<start_of_turn>user
En ingeniería rápida, ¿cuándo usar RAG o ajuste fino?<end_of_turn>
<start_of_turn>model
Prompt engineering is a critical aspect of working with language models like GPT-3 or GPT-4. The choice between using a Retrieval-Augmented Generation (RAG) approach or fine-tuning depends on the specific requirements and constraints of your task. Here's a breakdown of when to use each:

Use RAG (Retrieval-Augmented Generation) When:

You Need Up-to-Date Information: RAG combines the language model's generation capabilities with real-time retrieval of information from external sources. This is particularly useful if your task requires incorporating the latest information that might not be within the training data of the language model.
Complex Understanding Is Required: If your task involves understanding and integrating information from various sources or contexts, RAG can be beneficial as it retrieves and then generates responses based on a wide range of information.
You're Handling a Broad Range of Topics: For tasks that cover a wide range of topics, RAG's ability to pull in relevant information on-the-fly can be more efficient than training a model on a vast corpus of data.
Use Fine-tuning When:

You Have Specific Data or Domain: If your task is highly specialized and you have access to a corpus of data in this domain, fine-tuning the model on this data can lead to more accurate and relevant responses.
You Need Customized Responses: Fine-tuning allows you to tailor the model’s responses to fit the style, tone, or specific needs of your application.
Consistency is Key: Fine-tuned models can provide more consistent responses in a specific domain, as they are trained to understand and generate text based on the nuances of that domain.
Limited or No Internet Access: If your application will be used in environments with limited or no internet access, fine-tuning is preferable, as RAG requires real-time access to external sources.
In summary, use RAG for tasks that require up-to-date information, a broad understanding of various topics, or complex integration of different information sources. Opt for fine-tuning when you have specific domain data, need customized responses, value consistency, or operate in environments with limited internet access. Each method has its strengths, and the choice largely depends on the nature and requirements of your specific application or task.<end_of_turn><eos>