Prompts and training are two fundamental ways of inputting data to language models. While in the past, the best way to use a language model and fine tune it to execute specific tasks was mostly done by training the model using a (often quite large) labeled dataset, the emergence of large language models and generative AI made it possible to get great results using prompting.
In this blog post, we'll explore the differences between prompting and training, the advantages of each, and why generative AI opens exciting new possibilities.
Prompting vs. Training:
Training is the traditional way of inputting data into an AI model. It involves exposing a language model to large amounts of data, which it then uses to learn patterns and relationships that can be used to make predictions. During training, the model's parameters are adjusted to optimize its performance on a specific task or set of tasks. The goal of training is to improve the model's accuracy and make it more effective at generating an output that solves the task with accuracy.
Prompting, on the other hand, refers to the practice of providing initial input data to a language model in the form of a prompt or a set of instructions. The model then generates output based on the prompt, completing tasks or answering questions as required. In other words, prompting sets the direction for using the model's as it is, and the user's task is to find the best prompt to get the expected output.
Prompting is easy!
Prompting is often easier than training because it uses an already trained model and only relies on changing the request to this model instead of actually retraining the model itself to improve or fine tune the results it produces.
Training a language model also requires a dataset, with labels, which usually takes a lot of time to create, and can be a very time-consuming and computationally expensive process, especially for larger and more complex models. It can take weeks or even months to train a model on a large dataset, and the process requires specialized hardware and software.
Prompting offers a quick and easy way to generate output from a language model by utilizing the necessary computational power. By providing contextual data to the model through prompts, the generated answer can be fine-tuned dynamically, similar to re-training the model.
In conclusion
The use of prompts opens up new possibilities for language models, allowing for complex tasks to be completed with minimal effort and high-quality outputs to be generated. For instance, with refined prompts, it is possible to engage in a "chat" with any document. Users can ask questions and receive summarized answers that include all relevant information.
Prompting also enables the addition of contextual information, such as internal company data, to language models. This allows companies to leverage the text-generation capabilities of these models, even without access to the internal data typically required to provide relevant answers.
