Master LLMs with our FREE course in collaboration with Activeloop & Intel Disruptor Initiative. Join now!


Prompt Engineering Best Practices: Iterative Prompt Development
Data Science   Latest   Machine Learning

Prompt Engineering Best Practices: Iterative Prompt Development

Author(s): Youssef Hosni

Originally published on Towards AI.

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.

Iterative Nature of Prompt EngineeringSetting Working Environment & Getting StartedOvercoming Too-Long LLM ResultsForce the LLM to Focus on Certain DetailsGetting Complex Responses

Most insights I share in Medium have previously been shared in my weekly newsletter, To Data & Beyond.

If you want to be up-to-date with the frenetic world of AI while also feeling inspired to take action or, at the very least, to be well-prepared for the future ahead of us, this is for you.

U+1F3DDSubscribe belowU+1F3DD to become an AI leader among your peers and receive content not present in any other… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI

Feedback ↓