Introduction to Dageno AI Diagnostic Center (2) - Prompt Mining Panel & High-Heat Prompt Generation

Recently, many friends have sent private messages asking:

• "I'm currently monitoring 50 prompts, but how do I know which ones are truly valuable?"
• "I see that the competing product ranks very high, but I'm not sure if this issue is worth investing resources to optimize?"
• "I've come up with some prompts myself to monitor, but I always feel they might not be the questions users would actually ask?"

These issues point to the same pain point: In GEO, choosing the right problem is more important than optimizing it.

If the prompt you are monitoring does not have real demand to begin with, then subsequent visibility, mention rate, and competitive analysis will all lose their meaning. It's like opening a store on a road where no one walks; no matter how well-decorated it is, it won't work.

These are the two new features we will introduce in this article:

• Prompt Diagnostic Panel — Helps You Identify Which Prompts Have Low Value
• High-Heat Prompt Generation Agent — Helps You Find More Worthwhile Monitoring Alternatives

They address a complete closed loop: from identifying the problem prompt to finding a better prompt.

1. The most common pitfall in GEO: monitoring a bunch of questions that no one asks

Let's first talk about why this issue is so important.

Over the past few months, we have seen that many teams, when doing GEO, fall into a misunderstanding:

"Monitoring more prompts" is equivalent to "conducting more comprehensive GEO".

So they will spend a lot of time thinking about all possible relevant issues and then adding them all to the monitoring list.

It looks very substantial, with many charts and abundant data.

But the problem is that these prompts may not have any real demand at all.

For example:

Suppose you are a brand that does CRM, you might monitor a prompt like this:

"What are the top CRM features for enterprise teams?"

This question seems reasonable, doesn't it? It is relevant to your business and is also a reasonable user question.

But if this issue only has 20 real searches per month in the market you're focusing on (such as North America), then its optimization value is very limited.

In contrast, if there is another prompt:

"Which CRM integrates best with Slack and HubSpot?"

This question has 2,000 searches per month, so its optimization value may be 100 times that of the former.

That's why we say GEO is not about "monitoring more" but "monitoring right".

The problem is that when generating prompts, most GEO tools simply allow large models to freely improvise based on brand information. They do not tell you whether there are real needs behind these prompts, nor do they help you determine which ones deserve priority attention.

This is also the biggest pain point for users when using other GEO tools.

So what Dageno wants to solve is not to make you "monitor a few more words", but to make you monitor truly valuable words.

2. Prompt Diagnostic Panel: Conduct a "physical examination" on Prompts

Prompt Diagnostic Panel.

You can think of it as a "Prompt Health Check Tool". It will tell you: among all the prompts you are currently monitoring, which ones are worth continuing to pay attention to and which ones may have low value.

We don't just look at whether your brand is mentioned or how high it ranks.

We will also incorporate a very critical dimension: the actual monthly search volume of this prompt.

This search volume is not a wild guess, not a vague "popularity index", nor the general keyword volume level in traditional SEO.

It represents the real search demand of users for this prompt in the area you are currently monitoring this month.

For example:

• If a prompt has only 30 searches per month in the North American market, the system will label it as "low search volume"
• If a prompt has 3,000 searches per month in the area you are monitoring, the system will mark it as a "high-value opportunity"

This way, you can quickly see which prompts are worth further investment, and which may need optimization or replacement.

Our search volume data is sourced from real AI search behavior tracking (purchased from a third-party compliant plugin), covering 7 major mainstream large models (ChatGPT, Gemini, Perplexity, etc.), and is updated monthly by region.

This enables you to clearly see which issues have real, scalable demand behind them.

3. High-Heat Prompt Generation Agent: From Problem to Solution

Discovering the problem is only the first step. More importantly: if these prompts are not good enough, what should I replace them with?

This is the problem that the second feature aims to solve: High-heat prompt generation Agent.

When you click the "Action" button of a low-value prompt in the diagnostic panel, you will enter this Agent's workflow.

Simply put, this Agent will help you replace low-search-volume prompts with new prompts that have more real demand and are more worthy of monitoring.

Its workflow consists of three steps:

Step 1: Understand your brand and business boundaries

Agent will first try to understand: who you are, what you do, what your core business is, and what the topic you are currently focusing on is.

This step is important because not all high search volume prompts are suitable for your brand.

We don't chase hot topics just for the sake of chasing them, but rather aim to find prompts that are relevant to the brand and also meet real needs.

For example, if you are a brand specializing in CRM for small and medium-sized enterprises, a prompt like "enterprise-level CRM compliance features", even if it has high search volume, may not be suitable for you.

Step 2: Based on existing themes, explore better alternatives

The Agent will not randomly throw a bunch of keywords at you, but will instead help you optimize based on your original monitoring topic.

Step 3: Display the reference monthly search volume to help you make decisions

This is the most crucial step.

The Agent will present each newly generated prompt along with its corresponding reference monthly search volume to you.

You can directly see: how much real monthly search demand this new prompt has approximately, and whether it is worth adding.

This way, you no longer have to guess, but can make decisions based on real data.

4. A complete closed loop: from discovery to replacement

These two functions, when combined, address a complete problem.

The first panel (Problem Mining) addresses:

Among the words you are currently monitoring, which ones have low value and which ones need optimization?

The second Agent (high-heat generation) addresses:

If these words are not good enough, what new words that are more worthy of monitoring should I replace them with?

One is responsible for identifying problems. One is responsible for providing new directions.

This is also why we have always emphasized that Dageno is not just a GEO monitoring tool; what we really want to do is the data strategy layer of GEO.

Because for users, what is often the most difficult is not understanding a chart.

The hardest part is making judgments from data:

• Which prompts are really worth keeping?
• What is not worth it?
• Why isn't it worth it?
• What should I switch to next?
• Is there a real need behind these new words?
• Do these needs occur in the markets I truly care about?

These are the real factors that determine whether GEO can achieve results.

Suppose you are a brand that develops project management tools, and you are currently monitoring 30 prompts.

You open the prompt question mining panel and find that 8 prompts are marked as "low search volume".

One of them is:

"What are the best project management methodologies?"

You click "Action" to enter the high-heat prompt generation Agent.

After the Agent's analysis, 3 alternative solutions have been recommended for you:

• "Which project management tool is best for agile teams?"
  Reference monthly search volume: 3,200

• "Best project management software with Jira integration"
  Reference monthly search volume: 2,800

• "Free project management tools for small teams"
  Reference monthly search volume: 4,500

When you look at these data, you'll find that the 3rd prompt not only has the highest search volume but also highly matches your Target User (small teams).

So you decide to replace the original low-value prompt with this new one and start optimizing the content for this problem.

One month later, you find that the brand mention rate of this prompt has increased from 0% to 40%, and the ranking has improved from "not mentioned" to the 2nd place.

More importantly, since this prompt has 4,500 real searches per month, the actual exposure and traffic brought by this optimization are 90 times that of the previous low-search-volume prompt.

This is the value of "choosing the right question".

5. From Prompt Discovery to Content Growth

We also want to share with you that since the product was launched, we have been using Dageno AI to optimize our own official website.

For example, throughout April, we referred to the results provided by the diagnostic system, used the prompt Agent to generate new high-quality prompts, and finally used the writing Agent to complete the writing and publish it on the official website and external channels.

The final effect is not only reflected in GEO data.

Natural search traffic has also significantly increased (the following screenshots are from Semrush, and you can check them out on your own).

Some friends may worry that publishing articles written by AI Agents will be penalized by Google?

On May 15, 2026, at a Google official event held in Shanghai, a very important signal was once again clarified:

AI-generated content receives better search rankings than content produced by real people, but attention must also be paid to content quality.

That is to say, what search engines truly judge is not whether the content is AI-generated, but whether it is real, useful, professional, and trustworthy, and whether it truly solves users' problems.

Especially in Google's emphasized EEAT principle, Trust is placed in a very central position.

This also means that in future content competition, it is no longer just about "who writes more", but rather:

• who better understands user needs
• who can continuously produce high-quality, differentiated, and trustworthy content

And this is exactly the problem that Dageno AI can help users solve.

Dageno AI provides content agents for product growth.

It can help brands and products automatically detect content opportunities, analyze what is missing in the market, what competitors have covered, and what content entry points of your product are still worth being discovered by large language models/search engines and users.

After identifying opportunities, Dageno AI can further execute content production tasks: generating high-quality content that better aligns with search intent, is more structured, and is more trustworthy, centered around product selling points, user pain points, SEO keywords, industry trends, and real-world usage scenarios.

In other words, Dageno AI helps products establish a complete growth process from:

content opportunity discovery → content strategy judgment → content Agent execution → high-quality content output