AI has completely changed our approach to complex, data-driven problems in data science. Whether it’s building chatbots, automating content generation, or constructing highly use case-specific applications like diagnostic tools, the ability to optimize AI models is critically important. The two most disruptive methods for both optimizing AI models and harnessing the power of AI are fine-tuning and prompt engineering. Each has its distinct benefits relative to your goals, availability of resources, and timeline.

Key Takeaways

• Fine-tuning is the retraining of an AI model to make it more specific in a domain to gain additional accuracy.
• Prompt engineering is deliberately designing the input to create a better output, without needing to change the model.
• Fine-tuning requires more computational resources and data, while prompt engineering is typically fast and inexpensive.
• Using both processes may give the best outcomes to gain a trade-off between accuracy, speed, and cost.

What Are Fine-tuning and Prompt Engineering?

What Is Fine-tuning?
Fine-tuning takes an existing AI model and updates it with new, domain-relevant data. It’s like teaching a specialist another specialty…. Fine-tuning updates the models’ internal parameters by altering how it understands and generates output regarding highly specific scenarios. For instance, a general language model fine-tuned from a medical history will now be able to interpret request from healthcare areas better.

Key points about fine-tuning:

• Requires a large structured dataset related to the task.
• Needs substantial compute and time.
• Has a high accuracy to specialized problems.
• Requires expertise in machine learning and training the model.

What Is Prompt Engineering?
Prompt engineering refers to the synthesis of designing and optimizing the input queries or “prompts” you make to an AI model to gain the best output. Unlike fine-tuning, it is not changing the model but uses this inherent quality of how the model will respond to various instructions.

Features of prompt engineering:

• No retraining or additional data need.
• Fast iteration through prompt variation.
• Requires creativeness and understanding of model behavior.
• Very flexible for multiple tasks.

How Do They Differ Technically?

The two main differences are in method, resources, skills, and flexibility:

• Fine-tuning modifies the model, i.e. retrains the model’s parameters on new data. This step requires data preparation, training epochs, and evaluation.
• Prompt engineering modifies the input to the model in order to find a way to encourage an existing model to produce the desired output by creating more appropriate or context-rich prompts.

When to Use Fine-tuning or Prompt Engineering?

Accuracy vs. Flexibility

• Fine-tuning is best when you need the utmost accuracy and usable domain knowledge – think law, medical, or financial services and similar use cases.
• On the other hand, prompt engineering has advantages in flexibility, speed and prototyping, and resource-constrained use cases.

Typical Use Cases

Fine-tuning is best for:

• High-stakes, narrow-domain AI systems.
• Situations where the labeled data and compute resources are plentiful.

Prompt engineering is best for:

• Quick experimentation use cases, and multi-domain use cases.
• When you don’t want to incur the expense of re-training.

How Data Scientists Use These Techniques

Fine-tuning in Practice

ata scientists are involved with gathering and cleaning up domain-specific datasets, then making use of GPUs to re-train the models iteratively. The resulting model is very specific to the initial task, but this does not allow for further or other adaptation later. In some cases, fine-tuning models can take weeks or months..

Prompt Engineering in Practice

Data scientists are involved in writing, testing, and refining prompts to yield a desirable AI response. They typically use few-shot prompts by adding examples, or context, to the prompt to improve the quality of the model’s output. With the use of prompt engineering capabilities, the model can be deployed and adapted much faster.

Combining Fine-tuning and Prompt Engineering

Clever teams marry both methods together:

1. First, modify the model to be domain specific.
2. Then, do prompt engineering to fine-tune the right outputs for a particular situation   instead of retraining the model.
3.Balancing computational expense and speed with accuracy.

Conclusion

Fine-tuning and prompt engineering are both important techniques to improve AI models. Fine-tuning provides a deep level of customization and better accuracy, but takes longer, needs more data and more resources. Prompt engineering provides a quicker, flexible solution using effective inputs without changing the model. The one to choose will depend on what you need for your project and what you have available. Many data scientists use both to get the ultimate outcome.

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