Interesting AI use cases

In this post, I’m looking into the error-handling capabilities of four leading prompt management solutions, which are among the best prompt engineering tools available: PortKey, Agenta AI, LangFuse, and PromptLayer.

I’m going to simulate 5 real-world scenarios that are likely to occur if you’re working on a product with non-technical contributors working on a set of prompts via a prompt manager.

You are a software engineer working on an AI-powered product, and you’ve found yourself in a situation where your product team needs to adjust the wording on some of the prompts that have been hardcoded into the codebase. Maybe you are working on a smaller projects with a single client who wants a bit more control over the final wording of the prompts. The solution is to move the prompts out of your source code and into a dedicated prompt management system so that other people can work on them.

For most AI powered product teams, the final wording around prompts usually falls to a software engineer. There might be some R&D that goes into designing the prompts before they are handed to the engineers, but once the prompts are hardcoded in, the responsibility of maintaining them becomes the engineering department’s problem.

This is a problem because prompts are not like other snippets of code. Designing good prompts combines subject matter expertise, with current best practices around prompt engineering, and a technical understanding of how and where to implement these prompts into a codebase. A software engineer cannot be expected to play all three roles.