Optimize model performance

After you deploy your model to an endpoint, you can start interacting with it to see how it works. Let’s explore how you can use prompt engineering techniques to optimize your model’s performance.

Apply prompt patterns to optimize your model’s output

The quality of the questions you send to the language model, directly influences the quality of the responses you get back. You can carefully construct your question, or prompt, to receive better and more interesting responses. The process of designing and optimizing prompts to improve the model’s performance is also known as prompt engineering.

Prompt engineering requires users to ask relevant, specific, unambiguous, and well-structured questions, instructing the model to generate more accurate responses. To understand how to create well-defined prompts, let’s explore some patterns that help you improve the output of a model:

• Instruct the model to act as a persona.
• Guide the model to suggest better questions.
• Provide a template to generate output in a specific format.
• Understand how a model reasons by asking it to reflect.
• Add context to improve the accuracy of the model’s output.

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