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.