Foundation model customization
Important
For the latest information about model customization on Amazon SageMaker AI, see the dedicated Model customization section. We started by offering model customization through JumpStart, but have expanded our capabilities over time, prompting us to dedicate a new section.
For an overview of Amazon SageMaker AI JumpStart, see SageMaker JumpStart.
Foundation models are extremely powerful models able to solve a wide array of tasks. To solve most tasks effectively, these models require some form of customization.
The recommended way to first customize a foundation model to a specific use case is through prompt engineering. Providing your foundation model with well-engineered, context-rich prompts can help achieve desired results without any fine-tuning or changing of model weights. For more information, see Prompt engineering for foundation models.
If prompt engineering alone is not enough to customize your foundation model to a specific task, you can fine-tune a foundation model on additional domain-specific data. For more information, see Foundation models and hyperparameters for fine-tuning. The fine-tuning process involves changing model weights.
If you want to customize your model with information from a knowledge library without any retraining, see Retrieval Augmented Generation.
