> ## Documentation Index > Fetch the complete documentation index at: https://docs.refuel.ai/llms.txt > Use this file to discover all available pages before exploring further. # Overview ### What is LLM Finetuning? Finetuning is the process of taking a pre-trained language model (LLM) and further training it on a specific dataset to adapt it to a particular task or domain. This involves adjusting the model's weights based on the new data, allowing it to learn patterns and nuances specific to the desired domain. ### Benefits of Finetuning Finetuning offers several advantages when compared to using the base LLM directly: * **Improved Performance**: By training the model on task-specific data, finetuning can significantly enhance the model's performance on that task, leading to more accurate and relevant outputs. * **Customization**: Finetuning allows for the customization of the model to better align with specific requirements, such as industry jargon, user preferences, output style and format etc. * **Efficiency**: Once finetuned, the model can generate more precise results without needing extensive context in the input. This means lower token usage, making it faster and cheaper. ### When to Finetune Consider finetuning a base LLM in the following scenarios: * **Specialized Tasks**: When the task requires domain-specific knowledge or terminology that the base model may not be familiar with. * **High Accuracy Requirements**: When the application demands high accuracy and precision, such as in medical diagnosis, legal document analysis, or financial forecasting. * **Consistent Output**: When consistent and reliable output is crucial, and in-context learning may not provide the necessary stability. ### Prompt engineering vs Few-shot learning vs Finetuning When leveraging LLMs for specific tasks, you have multiple strategies to improve performance: * **Prompt Engineering**: The practice of crafting precise and optimized input prompts to achieve desired model outputs without modifying the underlying model. * **Few-shot Learning**: This involves providing the LLM with a few examples (input and expectedoutput pairs) along the guidelines in the prompt. One way to think about few-shot (in-context learning) is that it is a form of "reasoning by analogy". * **Finetuning**: This involves adjusting the LLM’s parameters by training it on a domain-specific or task-specific dataset. Each strategy has its own trade-offs and is best leveraged at different stages of development ([image credit](https://x.com/karpathy/status/1655994367033884672)): Finetuning vs Prompt Engineering