ChatGPT's Prompt Suggestions: Generative Engine Optimization’s First Real Tool

We’re witnessing the birth of an entirely new optimization discipline, and nobody has a manual.

While SEO marketers spent decades perfecting their craft with tools like Google Analytics, SEMrush, and Ahrefs, the world of Generative Engine Optimization is the digital Wild West. AI systems like ChatGPT don’t give us SERPs to analyze, rankings to track, or CTRs to obsess over. They just… respond. Instantly. Invisibly.

But buried in the interface of these black-box systems, there’s a signal hiding in plain sight – one that might just crack open the future of content optimization. Those innocuous autocomplete suggestions that pop up when you start typing? They’re not just helpful hints. They’re the Rosetta Stone of GEO.

Try this: Open ChatGPT and type “how to start a…” Notice what appears below the input field. You might see suggestions like “side business,” “garden,” “podcast,” or “morning routine.” These aren’t random – they’re curated nudges reflecting what the model thinks you want to ask and what it knows it can answer well.

These suggestions may look like a user-experience flourish, but they represent something more significant. They’re the first visible clues about how generative engines interpret and steer intent, and they may be the foundation on which GEO tooling will be built.

From Keywords to Prompts

Search engines reward keywords. Generative engines reward prompts – and that’s not a small shift.

In SEO, optimization meant improving your visibility across indexed search results. You had rankings to chase, links to build, metadata to fine-tune. In GEO, there is no SERP. A user types a prompt, and the engine responds immediately and invisibly. There’s no clear feedback loop. No CTR to analyze. No public ranking factors.

Prompt suggestions began appearing in ChatGPT sometime in early 2025, with their first widespread rollout in the mobile app during late Q1. While OpenAI hasn’t published formal documentation, community reports suggest this was the first real-time, in-product feedback mechanism generative engines offered to users – and to marketers.

For those trying to understand what these black-box systems want to be asked, prompt suggestions are a welcome anomaly: a rare, observable signal in an otherwise opaque process.

How Suggestions Actually Work

OpenAI hasn’t disclosed the exact mechanics, but based on standard LLM architecture and UI practices, prompt suggestions likely draw from several sources:

• User behavior patterns: What people frequently type and complete successfully
• Model confidence levels: Topics the model knows it can answer accurately
• Content policy filters: What’s deemed safe, appropriate, or brand-aligned
• Trending topics: What’s emerging in news, social media, or shifting user intent
• Geographic and demographic factors: Variations based on location and user history

This means suggestions aren’t just completions – they’re editorial decisions. They steer users toward prompt types the model can handle safely, accurately, and with high engagement.

Turning Suggestions Into GEO Strategy

Prompt suggestions reveal more than common queries. They show preferred phrasing, intent structure, and content frames the model has been trained to recognize and respond to well. For anyone practicing GEO, they offer three critical insights:

1. Common user intents the model is specifically trained and tuned for
2. Preferred language structures (“how to start a…” instead of “ways to begin…”)
3. Content areas being actively promoted or deprioritized

Here’s how agencies and content strategists can turn this into actionable intelligence:

Track Suggestions Systematically

Create a simple spreadsheet with columns for date, prompt stem, and suggestions returned. Start typing stems relevant to your industry: “How to choose a…”, “Best way to…”, “What happens when…” – and log what appears. Do this weekly across different devices and locations if possible.

For a financial services firm, typing “how to invest in…” might surface suggestions like “index funds,” “real estate,” and “crypto” but not “green bonds.” That gap could signal either insufficient training data or limited user interest – both useful for content planning.

Mirror the Model’s Language

If ChatGPT consistently suggests “How to build passive income” over “Ways to earn money while you sleep,” match its phrasing in your content. GEO rewards alignment with model patterns, not creative copywriting. The suggestions reveal the language structures the model recognizes and trusts.

Identify Content Opportunities

Topics absent from suggestions represent potential opportunities. If relevant queries in your domain never appear, it could indicate a content gap where well-structured, fact-based material might gain traction. This is especially true for emerging topics that haven’t reached critical mass in the training data.

Test Your Content’s Prompt Performance

After publishing generative-friendly content, return to the suggestions interface. Type queries related to your topic and see if your themes ever surface. If your carefully crafted content never appears reflected in suggestions, you may need to adjust your framing or approach.

Understanding the Limitations

Prompt suggestions aren’t perfect proxies for search volume. They don’t tell you how often something is typed, clicked, or converted. They’re curated predictions, not comprehensive data.

More importantly, because suggestions are filtered through safety systems and editorial decisions, they reflect the platform’s internal priorities. Topics that are controversial, underrepresented, or simply deemed risky may never appear, even if they’re valid and important to your audience.

This editorial layer means prompt suggestions don’t reflect an open market of ideas – they reflect confidence levels and content policies. Geographic location, user history, and demographic factors also likely influence what appears, making suggestions a partial rather than complete picture of user intent.

What’s Coming Next

Prompt suggestions are version 1.0 of GEO tooling. We can expect a rapid evolution toward:

• Prompt suggestion APIs giving developers structured access to suggestion data
• GEO dashboards that track prompt performance and model coverage over time
• Content optimization tools that simulate prompts and evaluate generative visibility
• Reverse prompt engineering tools that work backward from answers to infer optimal prompts

In other words, a full GEO stack comparable to what we have in SEO will emerge. The questions is when. Prompt suggestions are the first feature pointing us in that direction.

Start This Week

Prompt suggestions might look like a minor UI feature, but they’re the first observable signal of intent shaping in generative systems. They’re limited, curated, and imperfect – but they’re also the only real tool GEO practitioners can currently touch, test, and learn from.

If you’re serious about understanding how generative engines engage with content, start tracking suggestions in your domain this week. Create that spreadsheet. Test those prompt stems. Look for patterns in language and gaps in coverage.

It’s not a complete strategy, but it’s a concrete starting point. And in GEO, that’s exactly what we need right now.