# Foundation model customization
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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](jumpstart-foundation-models-customize-prompt-engineering.md).

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](jumpstart-foundation-models-fine-tuning.md). 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](jumpstart-foundation-models-customize-rag.md).