Fine-tuning updates model weights on your data. Prompt engineering steers a frozen model with better inputs.
Fine-tuning is a machine learning technique where a pre-trained model is adapted for a specific task using a smaller, task-specific dataset. This process involves further training the model on new data, allowing it to learn specialized patterns and nuances relevant to the target.
Fine-tuning adapts pre-trained AI models for specific tasks using new data, enhancing performance with less effort.
Full PathTerms™ definitionPrompt engineering is the discipline of designing and refining the inputs given to large language models (LLMs) to elicit desired outcomes. It involves carefully crafting instructions, providing context, and setting constraints to guide the AI's responses, moving beyond simple.
Prompt engineering is the art of crafting inputs for AI to get useful, safe, and repeatable results.
Full PathTerms™ definitionPeople mix up Fine-tuning and Prompt engineering in exam questions and job posts. Knowing the difference helps you pick the right control, process, or architecture choice.
Fine-tuning updates model weights on your data. Prompt engineering steers a frozen model with better inputs.
