How to test prompts effectively with AI tools

TL;DR

The video shows four practical ways to battle test prompts and an extra tool that ties them together. You can simulate conversations with a custom GPT, use Google Sheets with a GPT add-on, test static prompts in Airtable with Make, and run conversational prompts in Airtable using JavaScript. A bonus reveal promises a unified testing workflow.

Transcript

everyone talks about writing prompts but not how to test them this is actually the trickiest part where entrepreneurs and organizations always seem to get stuck it's one thing to write these prompts and it's a completely other thing to actually know how to battle test them effectively but what if you could have ai not only write your prompts but al... Read More

Key Insights

• A custom GPT can simulate a conversation to test how prompts perform in practice.
• GPT for Sheets lets you generate and test prompts directly inside a Google Sheet without coding.
• Airtable combined with Make enables static prompt testing at scale with automation.
• Air table with JavaScript enables evaluating conversational outputs and responses.
• Make.com workflows orchestrate cross tool testing and data capture.
• The video promises a bonus tool that unifies testing across methods.
• Each method is designed to be actionable with minimal setup.
• The presenter emphasizes testing as a core skill for prompt engineers at any level.
• More videos from this channel:
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  • How to Power Internal AI Apps Without API Fees
  • How to use reverse prompting to build a reusable prompt database
• More from this channel: Mark Kashef

Questions & Answers

Q: How can I use a custom GPT to simulate conversation for prompt testing

You can start by creating a custom GPT with a defined persona and instructions, then run a simulated back and forth between a user and the assistant to see how the prompt behaves in practice. This method helps you observe the natural flow, identify breakdowns, and adjust prompts to improve coherence, tone, and relevance. It is a hands on approach that reveals gaps beyond static prompts.

Q: What role does Google Sheets play in testing prompts

Google Sheets, with a GPT add on, provides a low code environment to generate meta prompts and execute them to produce potential outputs. You enter a goal in one cell, trigger a GPT function in another, and view the resulting prompt and response in adjacent cells. It is a fast way to iterate prompts without building a full app.

Q: How does Airtable combined with Make help with static prompt testing

Airtable connected to Make allows you to set up static prompts and run automated scenarios to test them. You can store prompts, run simulated results, and capture outputs in a structured way. This setup is scalable and repeatable, making it easier to compare how different prompts perform across models and datasets.

Q: What is the benefit of using JavaScript in Airtable for testing conversations

JavaScript in Airtable lets you customize how conversation outputs are generated and stored. It enables more complex logic for simulating user prompts and assistant responses, capturing iterative results, and feeding them back into the testing loop. This enhances fidelity of the simulations and provides richer data for refinement.

Q: What is the final reveal promised in the video

The final reveal promises a tool the presenter spent weeks developing to bring all testing methods together. While the exact nature of the tool is not described in detail here, the aim is to provide a unified workflow that simplifies running multiple testing approaches and comparing results across configurations.

Q: Can these testing methods be used by beginners

Yes, the methods are designed to be practical and actionable with varying levels of technical skill. Beginners can start with a custom GPT simulation or the Google Sheets add on, gradually adding Airtable automation and JavaScript as they gain comfort. The goal is to build a repeatable testing habit that yields faster learning and better prompts.

Q: How does the video suggest handling different personas in prompts

The video demonstrates simulating conversations with different personas to see how tone, vocabulary, and assumptions affect prompt outcomes. By modeling user and assistant personas, you can observe potential misinterpretations, bias, or misalignment, and adjust prompts or system messages accordingly for better alignment.

Q: What is the practical takeaway for prompt engineers from this video

The practical takeaway is that prompt testing should be an integrated part of the design process, not a afterthought. By using a mix of custom GPT simulations, sheet based testing, Airtable with Make, and JavaScript enhancements, you can rapidly iterate, compare model behavior, and improve reliability and effectiveness of prompts in real world use.

Summary & Key Takeaways

• This summary distills the four main prompt testing methods demonstrated in the video, focusing on practical setup and quick wins that can be implemented without heavy coding. It emphasizes testing conversations, using add ons, and integrating Airtable with automation to iterate prompts faster.

• The second point outlines how to simulate back and forth interactions to observe prompt behavior under different scenarios, including tone and persona changes, and the value of capturing this in a testable format like Airtable. It notes the end reveal as a consolidating tool.

• The final point highlights how the examples build a repeatable workflow for testing prompts across tools, enabling quick comparison across models and setups, and reinforcing that the audience can start applying these techniques today.