{"id":183316,"date":"2024-07-30T16:25:10","date_gmt":"2024-07-30T15:25:10","guid":{"rendered":"https:\/\/liora.io\/en\/?p=183316"},"modified":"2026-02-12T11:44:42","modified_gmt":"2026-02-12T10:44:42","slug":"fine-tuning-vs-prompt-engineering-whats-the-difference","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/fine-tuning-vs-prompt-engineering-whats-the-difference","title":{"rendered":"Fine tuning vs Prompt Engineering: What\u2019s the difference?"},"content":{"rendered":"\n

GPT-4, PaLM 2, BARD… advances in artificial intelligence are multiplying all the time. And above all, they are becoming accessible to all.<\/strong><\/p>\n\n\n\n

Not just data experts, but also business leaders, marketing managers or even students looking for a little help. However, using these generative AI solutions<\/strong> doesn’t just happen. The democratization of these solutions shows the importance of knowing how to optimize results. This is where Prompt Engineering<\/a> and Fine-Tuning come in. So what are they? What are the differences between these two concepts? That’s what we’re going to find out.<\/p>\n\n\n\n

What is Fine-Tuning?<\/h2>\n\n\n\n

Despite their name, AI models are not “intelligent”. Behind this supposed intelligence lies an ability to find relationships in large, complex data sets. But data volumes are such that large language models<\/a> (LLMs) depend on optimization techniques. These include fine-tuning.<\/p>\n\n\n\n

Definition of Fine-Tuning<\/h3>\n\n\n\n

Inherent in model pre-training,<\/strong> this technique aims to reinforce the specialization of a model that has been trained on a wider distribution of data. For there are models capable of performing generic tasks.<\/p>\n\n\n\n

And others capable of performing specific tasks.<\/strong> It’s precisely in the latter case that Fine-Tuning comes into its own. The idea is to respond specifically to the user’s needs. For example, a customer support chatbot<\/strong> for a clothing company will not be trained in the same way as a chatbot tasked with generating medical pre-diagnoses.<\/p>\n\n\n\n

The aim of Fine-Tuning<\/strong> is to optimize the performance of an existing model by re-training it on specific data. By adjusting the model’s weights and parameters, this technique enables an AI system to adapt to more specific tasks.<\/p>\n\n\n\n

Successful Fine-Tuning<\/h3>\n\n\n\n

To improve AI system performance through Fine-Tuning, special attention needs to be paid to two parameters:<\/p>\n\n\n\n