> For the complete documentation index, see [llms.txt](https://learn-with-nathan.gitbook.io/learnwithnathan/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://learn-with-nathan.gitbook.io/learnwithnathan/ai-concepts/fine-tuning.md).

# Fine-Tuning

Fine-tuning involves training the AI model further on specific datasets to optimize its performance for specialized tasks.

**Detailed Explanation:**

* **Purpose:** To adapt the model to unique use cases, such as legal document analysis, customer service, or medical diagnostics.
* **Process:** Fine-tuning adjusts the weights of the model using labeled data. This makes it more accurate for specific tasks without losing general-purpose abilities.
* **Challenges:** Requires high-quality and sufficiently large datasets. Fine-tuning can also be resource-intensive.

**Example:**\
Fine-tuning a model for customer support in banking might include training it on historical chat logs to handle specific queries like loan applications or account details.