Prompt Engineering vs. Fine-Tuning—Key Considerations and Best Practices

Large Language Models (LLMs) like GPT (Generative Pre-trained Transformer) are trained on very large datasets to comprehend context, generate coherent responses, and produce creative content. LLMs have revolutionized content creation, customer service, and natural language understanding.

To enhance their adaptability, organizations must customize the models to excel in specialized domain-related tasks, improve accuracy in niche areas, or conform to specific linguistic styles or regulatory requirements

That’s where tuning comes into play. Two notable techniques are fine-tuning and prompt engineering. Fine-tuning involves retraining the model on a specialized dataset to adapt responses to specific contexts or domains. Prompt engineering, on the other hand, modifies the input prompt to guide the model’s output without retraining for data—offering a less resource-intensive customization method.

This article explains fine-tuning and prompt engineering in detail and compares their usage.

Summary of key prompt engineering vs. fine-tuning concepts

The following table summarizes the key differences between prompt engineering and fine-tuning.

The rest of the article explains these points in detail.

Fine-tuning LLMs

Fine-tuning adapts Large Language Models (LLMs) to specialized tasks or domains, enhancing their ability to generate relevant and accurate outputs. This customization technique involves several critical steps, each contributing to the model’s refined performance.

Fine-tuning over a Base LLM model in a supervised fashion, utilizing a custom knowledge base (Source)

The fine-tuning process

The fine-tuning process involves the following steps.

Data selection and preparation 

Selecting high-quality, relevant data is an important first step. The data should closely mirror the tasks or contexts the model will encounter, encompassing diverse examples to cover various scenarios within the target domain. Preparing this data involves cleaning, labeling (if applicable), and possibly augmenting it to ensure the model has a robust dataset to learn from.

Hyperparameter adjustments 

You can alter the model’s hyperparameters or incorporate task-specific layers to better suit particular tasks. For example, you can add a specialized output layer for a classification task or fine-tune the learning rate. For very large models with  100 million to 1 billion+ parameters, alternative adaptation methods, such as Low-Rank Adaptation (LoRA), may also be used. LoRA is a popular and lightweight training technique that significantly reduces the number of trainable parameters. It works by inserting a smaller number of new weights into the model, and only these are trained. 

Training and optimization 

The crux of fine-tuning involves training the model further on the selected dataset. This step requires careful management of learning rates, batch sizes, and other training parameters to avoid overfitting. Overfitting occurs when a model becomes too attuned to the training data, impairing its ability to generalize to new, unseen data. 

Training techniques such as dropout, regularization, and cross-validation can help mitigate this risk of overfitting.

• Dropout randomly drops out a fraction of neurons (individual computational units within the neural network architecture) to prevent co-adaptation among neurons. 
• Regularization involves adding penalty terms to the loss function to discourage overly complex models and improve generalization performance on unseen data.
•  Cross-validation is a technique used to assess the generalization performance of LLMs by partitioning the available data into subsets, training the model on one subset, and evaluating its performance on another, iteratively rotating through all possible combinations to ensure robustness in model evaluation.

The optimization process also includes continuous monitoring of performance metrics to ensure the model is improving and adjusting training parameters as needed to optimize outcomes.

Example: Fine-tuning a sentiment analysis model

This example uses the Hugging Face transformers library to fine-tune a pre-trained BERT model on a sentiment analysis task. We’ll use a dataset from the datasets library for demonstration purposes.

! pip install torch
! pip install transformers
! pip install datasets

from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
from datasets import load_dataset
import torch

# Load the dataset (splitting into train and test)
dataset = load_dataset("imdb", split=['train', 'test'])

# Load the tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

# Tokenize the input (this can be optimized with a DataLoader for large datasets)
def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Format the dataset to PyTorch tensors
tokenized_datasets.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])

# Training arguments
training_args = TrainingArguments(
    output_dir='./results',          # output directory for checkpoints and model
    num_train_epochs=3,              # total number of training epochs
    per_device_train_batch_size=8,   # batch size per device during training
    per_device_eval_batch_size=8,    # batch size for evaluation
    warmup_steps=500,                # number of warmup steps for learning rate scheduler
    weight_decay=0.01,               # strength of weight decay
    logging_dir='./logs',            # directory for storing logs
    logging_steps=10,
    evaluation_strategy="epoch",     # perform evaluation at the end of each epoch
)

# Initialize the Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets[0],
    eval_dataset=tokenized_datasets[1]
)

# Train the model
trainer.train()

# Save the fine-tuned model
model.save_pretrained('./fine-tuned-model')

This example uses the IMDB dataset for sentiment analysis, which is available through Hugging Face’s datasets library. The dataset is automatically split into training and test sets. We load a pre-trained BERT model and its tokenizer. The tokenizer prepares the input data (text) for the model, and the model is then fine-tuned on the sentiment analysis task.

The tokenization process converts text into a format the model can understand, including padding and truncation to handle variable input lengths. TrainingArguments define various parameters for training, such as the number of epochs, batch size, and logging.

The Trainer class from Hugging Face simplifies the training and evaluation process. It takes the model, training arguments, and datasets to manage the training loop.

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Pros and cons of fine-tuning

Pros include:

• Enhanced accuracy and performance on domain-specific tasks.
• Ability to leverage pre-existing knowledge from the base model, reducing the need for extensive training from scratch.

On the other hand, the cons include

• Requires significant computational resources and time for huge models.
• Risk of overfitting, making the model less effective on general tasks or unseen data within the same domain.

Best practices

The foundation of effective fine-tuning lies in the dataset. Ensure your dataset is large enough to cover the domain comprehensively but is curated to avoid noise and irrelevant information. For example, when fine-tuning a sentiment analysis model, ensure the dataset comprises diverse, high-quality sentiment-labeled texts from reliable sources such as product reviews, social media posts, and sentiment-labeled datasets like IMDb or Yelp reviews.

Tools like Hugging Face provide access to a wide range of domain-specific datasets. Use tools like Hugging Face Trainer or Ray Tune for hyperparameter optimization to find the best learning rate, batch size, and other parameters.

One of the fine-tuning methods involves the gradual thawing of layers. Rather than training all layers simultaneously, you can gradually “unfreeze” layers of the model, starting from the top layers. This approach can lead to more stable and effective fine-tuning for certain use cases. Here’s how the gradual thawing of layers might be implemented in PyTorch with a model like BERT:

From transformers import AutoModel
model = AutoModel.from_pretrained('bert-base-uncased')
# Freeze all layers initially
for param in model.base_model.parameters():
    param.requires_grad = False

# Gradually unfreeze layers
layers_to_unfreeze = 2  # Example: Unfreeze the last 2 layers
for layer in model.base_model.encoder.layer[-layers_to_unfreeze:]:
    for param in layer.parameters():
        param.requires_grad = True

You should also continuously evaluate the model on a validation set during training to monitor performance and adjust training parameters as needed.

Prompt engineering LLMs

Unlike fine-tuning, prompt engineering does not directly adjust the model’s internal parameters. Instead, the process focuses on crafting and refining the input prompts that guide the model’s output. The term tuning might have suggested altering the model’s weights or architecture, but this is not the case with prompt engineering.

The prompt-tuning process

It involves an iterative process of refining the prompts based on the model’s outputs. This can include varying the prompt’s language, structure, or the information provided to find the most effective way of eliciting the desired response.

The effectiveness of prompt engineering lies in the art of crafting prompts that precisely communicate the task or desired output style to the model. This involves understanding how different prompt structures can influence the model’s responses.

The Do’s of Prompt Tuning with 3Cs. (Source)

Prompt engineering examples

Manual prompt engineering involves crafting prompts by hand to guide the model toward generating the desired output. It’s a creative process that relies on understanding how the model responds to different input types.

Example—generating a poem

prompt = "Write a poem about the sea:"
response = model.generate(prompt)
print(response)

Prompt templates use placeholders or variables that can be filled with specific information to generate tailored prompts. This approach allows for dynamic prompt generation based on the task’s context.

Example—product description

product_name = "Eco-friendly Water Bottle"
product_features = "reusable, made from recycled materials, keeps drinks cold for 24 hours"

prompt = f"Describe the product: {product_name}, which is {product_features}."
response = model.generate(prompt)
print(response)

Chain-of-thought prompting mimics a reasoning process, guiding the model to “think aloud” as it generates its response. This can be particularly useful for complex problem-solving or reasoning tasks.

Example—solving a math problem

problem = "If you have 3 apples and you buy 5 more, how many apples do you have?"

prompt = f"Let's think step by step to solve the problem: {problem}"
response = model.generate(prompt)
print(response)

Zero-shot learning and few-shot learning involve providing a prompt that instructs the model to perform a task it wasn’t explicitly trained on without any examples. Few-shot learning includes a few examples within the prompt to guide the model.

Zero-shot example—translation

prompt = "Translate to French: 'Hello, how are you today?'"
response = model.generate(prompt)
print(response)

Few-shot example—sentiment analysis

prompt = """1. "This movie is fantastic!" - Positive
2. "It was a terrible experience." - Negative
3. "I've had better meals." - Negative
4. "An utterly delightful concert!" - Positive
5. "The service at the restaurant is not good." - ?"""

response = model.generate(prompt)
print(response)

Soft prompt engineering involves learning a set of embeddings (soft prompts) that are prepended to the input to steer the model toward the desired output. The method typically requires gradient-based optimization but does not modify the original model parameters.

Example—custom embedding for a specific task

# Conceptual code snippet
soft_prompt = learn_soft_prompt("Summarize text")
input_text = "The history of Rome is complex and fascinating..."

prompt = soft_prompt + input_text
response = model.generate(prompt)
print(response)

This example is conceptual, as implementing soft prompt engineering requires a more complex setup involving optimization over embeddings.

Pros and cons of prompt engineering

Pros include:

• It is quick to implement and does not require retraining the model, saving computational resources.
• Flexible, allowing for rapid experimentation with different tasks or output styles.

Cons include:

• Fine-tuning allows models to learn from new examples and potentially acquire new knowledge not part of their original training set. In contrast, prompt engineering limits models to the original knowledge and capabilities and cannot introduce new knowledge.
• Effectiveness heavily depends on the skill in crafting prompts and may require extensive experimentation to achieve desired results.

Best practices in prompt engineering

• Design prompts that are clear, concise, and directly aligned with the task.
• Experiment with different prompt styles and formats.
• Use an iterative process to refine prompts based on model output, adjusting language, format, and instructions as necessary. Interactive notebooks like Jupyter allow for quick experimentation and iteration.
• Include a few examples in the prompt to guide the model when possible. This can significantly improve the model’s output by providing context.
• Develop templates for everyday tasks to standardize prompt structure, ensuring consistency and reducing the time needed to craft effective prompts.

Key differences—prompt engineering vs. fine-tuning

Larger models with more parameters may benefit more from fine-tuning due to their higher capacity for adaptation. However, they also require more resources to fine-tune effectively. Let’s consider some key factors when choosing between the two.

Accuracy

Fine-tuning generally achieves higher accuracy and precision on specialized tasks because the model’s parameters are directly optimized for those tasks. This is particularly evident in tasks requiring deep domain knowledge or nuanced understanding. 

The relevance, diversity, and size of the dataset used for fine-tuning significantly affects accuracy. High-quality, task-specific datasets lead to better fine-tuning results.

In contrast, the effectiveness of prompt engineering is limited by the quality and structure of the prompts. This can lead to slightly lower accuracy in tasks requiring specific expertise not fully covered during the model’s pre-training.

Flexibility

Once a model is fine-tuned for a specific domain, adapting it to another domain requires retraining, which can be resource-intensive. This makes fine-tuned models less flexible for rapid deployment across diverse tasks.
In comparison, prompt engineering offers greater flexibility, as changing the task often only requires modifying the prompt. This adaptability makes it easier to use a single model across various tasks without additional training.

Resource investment

Fine-tuning requires substantial computational resources and data, making it a more significant investment. Organizations must weigh this against the potential benefits of increased accuracy and specificity in model outputs.
Prompt engineering allows for rapid deployment across various tasks with minimal resource expenditure, offering flexibility and speed that can be crucial for certain applications or environments with limited computational capabilities.

Ethical considerations

The deployment of customized LLMs can have profound societal impacts, including privacy concerns, misinformation spread, and job displacement. Responsible usage policies and transparency are vital to mitigating these risks.
Both techniques can inadvertently reinforce biases present in the training data. It’s essential to carefully curate datasets and consider the ethical implications of model outputs.
In general, fine-tuning offers more control over model training to reduce bias.

Use cases—Prompt engineering vs. fine-tuning

Fine-tuned LLMs excel in simulating human-like conversations and providing contextually relevant responses in chatbots and conversational Agents. They can also accurately classify text sentiment, facilitating market research and customer feedback analysis.

In contrast, prompt-tuned LLMs generate precise search results and responses to user queries. Content creators use prompt engineering to generate articles, stories, and product descriptions tailored to specific requirements. Prompt-tuned models also provide accurate answers to questions based on provided prompts, enhancing information retrieval.

In practical applications, the decision isn’t simply a matter of choosing one approach over the other. For most enterprise scenarios, fine-tuning is essential to ensure data governance and minimize the risk of errors in LLM responses. Enterprise AI teams often employ a blend of fine-tuning and prompt engineering to meet their objectives effectively. The choice largely depends on the quality and accessibility of your data, with fine-tuning offering superior results due to its ability to customize models to specific needs and contexts deeply. Thus, promoting fine-tuning is crucial for achieving the highest accuracy and reliability in enterprise applications.

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Summary

Both fine-tuning and prompt engineering play a critical role in the future development/enhancement of generative AI applications. The decision on which to choose should align with the AI task’s specific goals, available resources, and operational constraints. Fine-tuning is the preferred route for enterprise AI applications requiring high accuracy and deep domain knowledge. Prompt engineering is a supporting activity that offers an efficient and flexible alternative for quickly adapting the fine-tuned model to various use cases.