Google has been rapidly releasing generative AI and related products recently, with Google AI Studio (1) particularly standing out as a developer platform. It integrates the latest image and video generation AI, truly embodying a multimodal platform. What's more, it's free up to a certain limit, making it a powerful ally for startups like ours. So, let's actually create an AI application with this platform!

1. Google AI Studio Portal

Below is the Google AI Studio portal. It has so many features that an AI beginner might get confused without prior knowledge. I suppose that's why it's a developer-oriented platform. By clicking the button in the red box, you'll be taken to a site where you can create an application simply by writing a prompt.

Here's the prompt I used this time.

"As a 'Complaint Categorization Agent,' you are an expert at understanding which product a customer is complaining about. You can select only one product from the complaint. Comprehensively analyze the provided complaint and classify it into one of the following categories:

Mortgage
Checking or savings account
Student loan
Money transfer, virtual currency, or money service
Bank account or service
Consumer Loan

Your output should be only one of the above categories. All samples must be classified into one of these classes. Results for all samples are required. Create a GUI that adds the ability to input a CSV file of customer complaints and generate a graph showing the distribution of customer complaint classes. Add features to the GUI to add labeled data independently of the customer complaint CSV file, calculate and display accuracy, and display a confusion matrix of the results."

Just by typing this prompt into the box and running it, the application described below is created. I didn't use any coding like Python at all. It's amazing!

2. Tackling a Real Classification Task with the Created App

After two or three attempts, the final application I built is shown below. It handles the task of classifying bank customer complaints by financial product. This time, I've set it to six types of financial products, but generative AI can achieve high accuracy even without prior training, so it's possible to classify many more classes if desired.

We import customer complaints via a CSV file. This time, I'll use 100 complaints. Furthermore, if ground truth data is available, I've added functionality to output accuracy and a confusion matrix. Below are the actual classification results. The distribution of the six financial products is displayed. It seems this customer complaint data primarily concerns mortgages.

Here's the crucial classification accuracy. This time, we achieved over 80% accuracy, at 83%, without any prior training. It's incredible!

The confusion matrix, often used in classification tasks, can also be displayed. This not only provides a numerical accuracy but also shows where classification errors frequently occur, making it easier to set guidelines for improving accuracy and enabling more effective improvements.

Confusion Matrix

3. Agent Evaluation

What I realized when creating this app was that if some evaluation metric is available, the quality of discussions for subsequent improvements deepens. Trying with just a few samples won't give a good grasp of the generative AI's behavior. Ideally, preparing at least 10, and ideally 100 or more, samples with corresponding ground truth data, and having the AI app output evaluation metrics, would enable effective accuracy improvement suggestions. This theme is called "Agent evaluation," and I believe it will become essential for building practical AI applications in the future.

What do you think? Despite not doing any programming at all this time, I was able to create such an amazing AI application. Google AI Studio integrates perfectly with Google Cloud, allowing you to deploy your app to the cloud with a single button and use it worldwide. Toshi Stats will continue to challenge ourselves by building various AI applications. Stay tuned!

1) Google AI Studio

Notice: ToshiStats Co., Ltd. and I do not accept any responsibility or liability for loss or damage occasioned to any person or property through using materials, instructions, methods, algorithms or ideas contained herein, or acting or refraining from acting as a result of such use. ToshiStats Co., Ltd. and I expressly disclaim all implied warranties, including merchantability or fitness for any particular purpose. There will be no duty on ToshiStats Co., Ltd. and me to correct any errors or defects in the codes and the software.

Hello everyone. How often do you find yourselves writing prompts? I imagine more and more of you are writing them daily and conversing with generative AI. So today, we're going to look at the state of cutting-edge prompt engineering, using a case study from a Silicon Valley startup. Let's get started.

1. "Parahelp," a Customer Support AI Startup

There's a startup in Silicon Valley called "Parahelp" that provides AI-powered customer support. Impressively, they have publicly shared some of their internally developed prompt know-how (1). In the hyper-competitive world of AI startups, I want to thank the Parahelp management team for generously sharing their valuable knowledge to help those who come after them. The details are in the link below for you to review, but my key takeaway from their know-how is this: "The time spent writing the prompt itself isn't long, but what's crucial is dedicating time to the continuous process of executing, evaluating, and improving that prompt."

When we write prompts in a chat, we often want an immediate answer and tend to aim for "100% quality on the first try." However, it seems the style in cutting-edge prompt engineering is to meticulously refine a prompt through numerous revisions. For an AI startup to earn its clients' trust, this expertise is essential and may very well be the source of its competitive advantage. I believe "iteration" is the key for prompts as well.

2. Prompts That Look Like a Computer Program

Let's take a look at a portion of the published prompt. This is a prompt for an AI agent to behave as a manager, and even this is only about half of the full version.

Here is my analysis of the prompt above:

Assigning a persona (in this case, the role of a manager)
Describing tasks clearly and specifically
Listing detailed, numbered instructions
Providing important points as context
Defining the output format

I felt it adheres to the fundamental structure of a good prompt. Perhaps because it has been forged in the fierce competition of Silicon Valley, it is written with incredible precision. There's still more to it, so if you're interested, please view it from the link. It's written in even finer detail, and with its heavy use of XML tags, you could almost mistake it for a computer program. Incredible!

3. The Future of Prompt Engineering

I imagine that committing this much time and cost to prompt engineering is a high hurdle for the average business person. After learning the basics of prompt writing, many people struggle with what the next step should be.

One tip is to take a prompt you've written and feed it back to the generative AI with the task, "Please improve this prompt." This is called a "meta-prompt." Of course, the challenges of how to give instructions and how to evaluate the results still remain. At Toshi Stats, we plan to explore meta-prompts further.

So, what did you think? Even the simple term "prompt" has a lot of depth, doesn't it?As generative AI continues to evolve, or as methods for creating multi-AI agents advance, I believe prompt engineering itself will also continue to evolve. It's definitely something to keep an eye on. I plan to provide an update on this topic in the near future.

That's all for today. Stay tuned!

ToshiStats Co., Ltd. offers various AI-related services. Please check them out here!

Prompt design at Parahelp, Parahelp, May 28, 2025

I tried creating and implementing an AI app with no-code on Google AI Studio, and it was amazing!

1. Google AI Studio Portal

2. Tackling a Real Classification Task with the Created App

3. Agent Evaluation

The Cutting Edge of Prompt Engineering: A Look at Silicon Valley Startup