Advanced Prompt Engineering Techniques in Google App Script with AI Language Models


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As you work with AI language models like GPT-3.5 Turbo and GPT-4 in your Google App Script applications, mastering advanced prompt engineering techniques can help you achieve better results and unlock the full potential of these models. In this blog post, we will delve into advanced prompt engineering strategies and explore how to apply them effectively when using Google App Script with AI language models.

Advanced Techniques

Technique 1: Conditioning the Model with System Messages

One way to condition AI language models to produce the desired output is by using system messages in your prompts. System messages act as instructions for the AI, guiding its behavior and output without being a part of the conversation or content.

For example:

const prompt = [{ role: 'user', content:'You are an AI language model that provides concise and accurate information.'},{role:'user', content: 'What are the main types of renewable energy?'}];

By incorporating a system message, you remind the AI of its purpose and help set the tone for the generated response.

Technique 2: Experiment with Prompt Polarity

Prompt polarity refers to how you phrase a question or statement, which can be either positive or negative. By experimenting with different polarities, you can influence the AI’s response and potentially improve its accuracy.

For example, you can rephrase a question from negative to positive:

const promptA = 'Why is it not a good idea to consume too much sugar?';
const promptB = 'What are the benefits of reducing sugar intake?';

By testing both prompts, you can identify which one yields better results for your application.

Technique 3: Chain Prompts Together

In some cases, breaking a complex task into smaller subtasks can improve the AI’s performance. You can do this by chaining prompts together, where the output of one prompt serves as the input for the next prompt.

For example, you may want the AI to summarize an article and then provide a recommendation based on the summary. Instead of using a single prompt, you can first create a summary and then ask the AI for a recommendation based on that summary.

const articleText = '...'; // The article text
const prompt1 = 'Summarize the following article: '+articleText;

// Obtain the summary from the AI's response
const summary = '...';

const prompt2 = "Based on the summary: "+summary+", what do you recommend for someone interested in the topic?";

Technique 4: Using Prompts as Filters

You can use prompts as filters to refine and narrow down the AI-generated content. This technique can be helpful when you want the AI to generate text that adheres to specific criteria or when you need to exclude certain information.

For example, you can ask the AI to provide recommendations but filter out any options that are too expensive:

const prompt = 'List some affordable travel destinations for a budget-conscious traveler.';

Technique 5: Anchoring Prompts with Examples

Anchoring prompts with examples is a powerful technique to provide context and improve the AI’s understanding of your expectations. By including examples in your prompt, you guide the AI to generate content similar to those examples.

For example, when generating slogans for a product, you can provide examples of successful slogans:

const prompt = `
Please generate a catchy slogan for a new brand of eco-friendly toothbrushes. Here are some examples of successful slogans from other products:
1. "Just Do It" - Nike
2. "Think Different" - Apple
3. "I'm Lovin' It" - McDonald's


Mastering advanced prompt engineering techniques is essential to unlocking the full

potential of AI language models like GPT-3.5 Turbo and GPT-4 in your Google App Script applications. By applying these advanced strategies, you can significantly improve the quality and relevance of the generated content and achieve better results in various use cases.

Remember that prompt engineering is an iterative process, and it’s crucial to experiment with different approaches to find the optimal prompts for your specific application. Continuously refining your prompts and applying advanced techniques will help you harness the power of AI language models and enhance your Google App Script applications.

By understanding and applying these advanced prompt engineering techniques, you will be better equipped to tackle complex tasks and challenges when working with AI language models. Stay curious, keep experimenting, and always strive to improve your prompt engineering skills for the best possible outcomes with AI language models in your Google App Script applications.