Automated Prompt Discovery: Find the Right Questions to Track in AI Search

Why prompt selection matters

The prompts you track in Superlines define your entire AI search intelligence context. They determine which competitive data you see, which content gaps surface, and what strategic actions the platform recommends. Getting this right is the difference between actionable intelligence and noise.

The challenge is real: manually selecting prompts risks tracking questions nobody actually asks. But exact keyword matching does not apply here — AI search is conversational, and people phrase the same intent in many ways. What matters is covering the intent clusters that drive visibility for your brand.

This guide covers three approaches to prompt discovery, from manual to fully autonomous, so you can choose the right level of automation for your team.

Approach 1: Import from existing data

If your organization already has marketing, support, or SEO data, you have a head start. These internal sources contain the exact language your customers use, which maps directly to the prompts AI models receive.

What to import

Source	What it contains	How to use it
Marketing messaging frameworks	Key value propositions, competitive differentiators, buyer persona questions	Import as strategic prompts organized by brand and persona
FAQ databases	Real questions customers ask repeatedly	Import directly — these mirror how users query AI models
Support ticket data	Pain points, product questions, how-to requests	Extract recurring themes and convert to prompt format
SEO keyword data	High-volume search terms, long-tail keywords	Transform keywords into conversational prompts using AI
Sales enablement content	Objection handling, comparison questions	Import as competitive tracking prompts

How to import

In Superlines, navigate to your brand’s prompt management section and use the bulk import feature. Organize prompts with labels to keep your tracking structured:

By funnel stage — awareness, consideration, decision
By topic cluster — pricing, features, comparisons, how-to
By competitor — prompts where specific competitors appear
By priority — strategic prompts you must win vs. monitoring prompts

Using MCP to bulk-add prompts

If you have a large dataset, the MCP server can accelerate this. In Claude Desktop or Cursor with the Superlines MCP connected:

I have a list of 50 keywords from our SEO tool. Convert each one into a 
natural conversational prompt that someone would ask an AI assistant, then 
add them all to Superlines tracking for [Brand Name] with the label 
"seo-import-feb-2026".

Here are the keywords:
[paste your keyword list]

The AI assistant will convert keywords like “best CRM software small business” into natural prompts like “What’s the best CRM software for a small business?” and add them via the add_prompts MCP tool.

Approach 2: Prompt Radar — automated discovery

Prompt Radar is a Superlines feature that automatically identifies trending prompts relevant to your brand that you are not yet tracking. It works by analyzing SEO, SERP, and People Also Ask (PAA) data sources to find questions with real search volume and AI search relevance.

How Prompt Radar works

Superlines analyzes your existing tracked prompts and brand context
It cross-references SEO, SERP, and PAA data to find related questions with proven search demand
It filters out prompts you already track
It surfaces new prompt suggestions ranked by estimated relevance

When to use Prompt Radar

Prompt Radar is especially valuable for teams that:

Don’t have large internal datasets to seed their prompt portfolio
Want to discover “hot” prompts — trending questions where AI search visibility matters most
Need to expand beyond their current tracking into adjacent topic areas
Want to ensure they’re not missing high-volume prompts that competitors are winning

Enabling Prompt Radar

In your Superlines organization settings, enable the Prompt Radar feature. It will begin suggesting prompts within your next analysis cycle. Review suggestions regularly and add the ones that align with your strategy.

Approach 3: Google Search Console integration

Google Search Console (GSC) provides a direct signal of what real users search for when they find your website. Importing high-performing GSC queries as prompts creates a bridge between your traditional search performance and AI search tracking.

What to look for in GSC data

GSC signal	Why it matters for AI search
High impressions, low clicks	People see you in search but don’t click — these queries likely also appear in AI search where click behavior differs
High click queries	Your proven performers — track these in AI search to protect your position
Rising queries	Emerging demand signals — get ahead by tracking these before competitors
Question-format queries	Already in conversational format — import directly as AI search prompts

Import workflow

Superlines supports GSC integration that can automatically suggest prompts based on your search console data. Connect your GSC account in the integrations settings and filter by the signals above.

Approach 4: MCP-powered autonomous discovery

The most advanced approach combines external data sources with the Superlines MCP server in an automated agent workflow. This is ideal for teams that want continuous prompt discovery without manual effort.

The discovery flow

External Data Source          AI Agent              Superlines
(SERP scraper, keyword       (Claude, Cursor,      (MCP Server)
 tool, GSC API)              custom agent)

     │                            │                      │
     │  1. Fetch trending         │                      │
     │     keywords/queries       │                      │
     ├───────────────────────────►│                      │
     │                            │                      │
     │                            │  2. Check which      │
     │                            │     prompts are      │
     │                            │     already tracked  │
     │                            ├─────────────────────►│
     │                            │◄─────────────────────┤
     │                            │                      │
     │                            │  3. Analyze gaps:    │
     │                            │     which trending   │
     │                            │     queries are      │
     │                            │     missing?         │
     │                            │                      │
     │                            │  4. Add new prompts  │
     │                            │     to tracking      │
     │                            ├─────────────────────►│
     │                            │                      │

Example: SERP-driven prompt discovery

If you have a SERP scraper MCP server (like DataForSEO or Bright Data) connected alongside Superlines, you can run this workflow in Claude Desktop:

Step 1: Use the SERP tool to find the top 20 trending search queries 
in the "[your industry]" category for [your country] this month.

Step 2: For each query, check with Superlines whether we're already 
tracking a similar prompt for [Brand Name].

Step 3: For any trending queries that we're NOT tracking, convert them 
into natural conversational prompts and add them to Superlines with the 
label "serp-discovery-feb-2026".

Step 4: Summarize what you added and why each prompt matters.

Example: Keyword-to-prompt conversion

Long-tail keyword data from SEO tools can be transformed into AI search prompts at scale:

I have keyword data with search volumes from our SEO tool. For each keyword 
with monthly search volume above 500:

1. Convert it into 1-2 natural conversational prompts that a user would 
   ask an AI assistant
2. Check if we're already tracking similar prompts in Superlines for 
   [Brand Name]
3. Add any new prompts with the label "keyword-discovery" and include 
   the original keyword and search volume in the notes

Keywords:
[paste keyword data with volumes]

Prompt variations

Superlines offers a Prompt Variations feature (enabled in organization settings) that automatically tests different phrasings of the same prompt without breaking the intent or specifics. For example, “Where can I easily get YEL insurance?” might also be tested as “Tell me where I can easily buy YEL insurance.” This ensures your tracking captures visibility across the natural variation in how people phrase questions.

Building a continuous discovery system

The most effective prompt strategy combines all four approaches into a continuous cycle:

Weekly rhythm

Day	Action	Approach
Monday	Review Prompt Radar suggestions, add relevant ones	Automated discovery
Wednesday	Check GSC for rising queries, import new ones	GSC integration
Friday	Run MCP agent to cross-reference external keyword trends	Autonomous discovery

Monthly review

Audit prompt performance — Use get_best_performing_prompt to find your top performers and get_competitive_gap to find where you’re losing
Prune low-value prompts — Remove prompts with zero relevance to your business
Expand into adjacent topics — Use fan-out query data to discover related topics AI models associate with your prompts
Update labels — Keep your organization system clean as the portfolio grows

MCP automation prompt for monthly review

Run a complete prompt portfolio health check for [Brand Name]:

1. List our top 10 best-performing prompts and why they're working
2. List prompts where we've lost visibility in the last 30 days
3. Show fan-out queries from our top prompts that reveal topics we're 
   not yet tracking
4. Recommend 10 new prompts to add based on competitive gaps and 
   content opportunities
5. Recommend any prompts to archive (low volume, no competitive relevance)

What comes after discovery

Once you have the right prompts tracked, the real power of Superlines unlocks:

Fan-out query intelligence reveals what AI models actually search for when answering your prompts — see our Fan-Out Query Competitive Intelligence guide
Competitive gap analysis shows where competitors are winning and what content earns them citations
Content pipelines can be built on top of this intelligence to automatically create optimized content — see our Agentic AEO Content Pipeline guide
Automated workflows chain these insights together for continuous optimization — see Automate GEO Analysis and Content Creation

The prompts you track are the foundation. Everything that follows — competitive intelligence, content strategy, autonomous optimization — depends on getting this right.