Fine Tuning vs Prompt Engineering

 

Aspect	Fine Tuning	Prompt Engineering
Definition	A process of adapting a pre-trained model to a specific task or domain by training it on a new dataset related to that task.	The design of specific prompts or instructions given to a pre-trained model to guide its output in a desired direction.
Objective	To make a pre-trained model more task-specific by adjusting its parameters based on the new task's data.	To influence the output of a pre-trained model by providing structured or specific input prompts.
Data Requirement	Requires a labeled or domain-specific dataset to train the model further for the target task.	Does not necessarily require additional data; it primarily involves crafting text prompts or inputs.
Scope	Adapts a model to perform well on a specific task or domain but still retains its pre-trained knowledge.	Influences the model's behavior or output by framing the input text with specific instructions.
Customization	Allows for task-specific customization of the model's behavior.	Customizes the model's output by specifying the format and content of the input prompt.
Examples	Fine-tuning a pre-trained language model for text classification, translation, summarization, etc.	Designing prompts for chatbots, question-answering models, and content generation models.
Flexibility	Offers flexibility in adapting a model to various tasks or domains with data availability.	Provides flexibility in influencing model output without the need for extensive retraining.
Training Process	Involves training on a new dataset, typically using standard training techniques.	Involves crafting prompt strings without retraining the model, relying on its pre-existing knowledge.
Resource Intensity	Can be resource-intensive due to model training on new data.	Typically less resource-intensive since it doesn't involve extensive model updates.
Use Cases	Commonly used when the model needs to perform specific tasks with high accuracy.	Applied when you want to control and guide the model's responses for user interaction.
Real-time Interaction	Requires retraining for changes in task or domain, making real-time adaptation challenging.	Allows real-time, on-the-fly control of model responses by modifying prompts during interaction.

In summary, fine-tuning focuses on retraining a pre-trained model on new data to adapt it to a specific task, while prompt engineering leverages specific text prompts to guide the model's output without extensive retraining. Both techniques have their unique use cases and are often used in conjunction for task-specific AI applications.