Prompt Engineering for Instruction-Tuned LLM: Iterative Prompt Development
When you build applications with large language models, it is difficult to come up with a prompt that you will end up using in the final application on your first attempt.
However as long as you have a good process to iteratively make your prompt better, then you’ll be able to come to something that works well for the task you want to achieve.
You may have heard that when training a machine learning model, it rarely works the first time. Prompting also does not usually work from the first time. In this article, we will explore the process of getting to prompts that work for your application through iterative development.
Table of Contents:
Iterative Nature of Prompt Engineering
Setting Working Environment & Getting Started
Overcoming Too-Long LLM Results
Force the LLM to Focus on Certain Details
Getting Complex Responses
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