Prompt Engineering Vs finetuning

Limitations of prompt engineering

Prompt engineering involves crafting specific input prompts to guide an LLM to provide better outputs. While it can be an effective way to improve performance, there are some limitations due to number of examples in a prompt due to token size:

Context Length: As you add more data examples to the prompt, the token length of the prompt increases. LLMs have a maximum tokens context length, which can restrict the number of examples you can provide in a single prompt.
Cost: With increased tokens in the prompt, the computational cost of processing the input also increases. This can lead to higher expenses as it is based on the number of tokens processed.
Accuracy: Accuracy of the LLM depends on number of examples provided in the prompt. But you can only provide few examples in a single prompt. If you have a big dataset, you cannot use all the data in one prompt due to context length limitation. This limits the possibility to increase the overall accuracy and effectiveness of the LLM.
Latency: As the prompt token length and computational cost increase, the latency of text generation also increases. This means that it may take longer to generate a response, which can be problematic in real-time applications such as chatbots or voice assistants.
Overfitting: Prompt engineering can also lead to overfitting, where the LLM becomes too specialized to the specific prompts provided and may not generalize well to new data. This can be mitigated by using a diverse set of prompts and regularly updating them, but it is still a potential limitation to consider.
Need for bigger LLMs: When you have few prompts, you will be opting for bigger LLMs to improve the performance. This will increase the latency, cost, and resource requirements.

Why Finetuning

Finetuning offers several benefits over prompt engineering:

Ability to use Large Dataset: Finetuning allows you to use a larger dataset for training, which can lead to better performance. You are not limited by the context length of the prompt as with few-shot learning.
Performance: Finetuned LLMs can achieve higher performance on specific tasks compared to using genericLLMs that rely solely on prompt engineering. For example, if you want to create a chatbot that can answer customer inquiries, finetuning with a dataset of customer inquiries can lead to better performance than relying solely on prompt engineering.
Small finetuned LLMs Perform Better: When doing finetuning with a large dataset for a specific task, small LLMs can match or surpass the performance of big LLMs.
Cost Reduction: With small LLMs, we can reduce the computational costs drastically as they don't require as much as computational power. Also you don't need to provide as many examples in the prompt, reducing the number of tokens processed and the associated costs.
Latency: Finetuning can also lead to reduced latency compared to prompt engineering, as the LLMs can be smaller and they are way faster than big LLMs.
Generalization: Finetuning can help the LLM generalize better to new, unseen data, as it has been trained on a more diverse set of examples.
Control: Finetuning an LLM allows for better control over the behavior and output. With finetuning, you can guide it to generate responses that are more relevant and appropriate for the task at hand. For example, if you can finetune an LLM to create support chatbot that follows a specific brand voice.

Prompt Engineering Vs Finetuning comparison

Aspect	Prompt Engineering	Finetuning
LLM Size	Requires big LLMs	Small LLMs can provide better performance
Number of examples	Restricted by context length of the LLM	Can handle larger datasets
Cost	Can be higher due to increased token length and using big LLMs	Lower, as small LLMs are cheaper and less tokens in the prompt
Performance	Limited by the quality of the prompt and number of examples	Can achieve higher performance with larger datasets
Latency	Can be higher due to big LLMs	Lower, as small LLMs are faster
Generalization	Limited by the diversity of few-shot examples	Can generalize better to new, unseen data
Control	Limited control over behavior and output	Better control over behavior and output

Which one to choose?

The choice between prompt engineering and finetuning depends on your specific use case and requirements:

Prompt Engineering: Choose prompt engineering if you have a small dataset or need a quick solution without investing in additional training. For example, if you want to create a simple question-answering system using a pre-trained LLM, you can craft a prompt like "Question: What is the capital of France? Answer: " and let the LLM complete the answer.
Finetuning: Choose finetuning if you have a larger dataset and want to achieve higher performance on a specific task. For example, if you want to create a sentiment analysis AI model for movie reviews, finetuning a small LLM on a large dataset of labeled movie reviews will likely yield better results than relying on prompt engineering alone.

In summary, prompt engineering can be effective for improving performance with limited datasets, but it has limitations due to token size and can lead to overfitting. Finetuning, on the other hand, allows for better performance with larger datasets, reduced costs and latency, better generalization, and more control over the behavior and output. So, choose the one that suits your needs the best!