Reverse-Engineering the LLM: A Guide to Selecting High-Value Queries for GEO

Traditional SEO keyword research is becoming less predictable. AI search engines like ChatGPT, Perplexity, and Gemini operate on reasoning chains rather than simple string matching.

This shift changes how we define targets. Instead of volume-based keyword lists, engineering teams and marketers need to build Prompt Portfolios. These are sets of intent-based queries that trigger specific reasoning paths where your product is the logical solution.

The Engineering of Intent Resolution

When a user executes a query like "My startup is scaling fast and our spreadsheets are breaking. What should I do?", the LLM processes it through a multi-step resolution chain. It does not simply look for the string "spreadsheets breaking."

The model typically performs these operations:

1. Intent Classification: Identifies the user's state (growth phase, technical debt).
2. Entity Association: Maps "spreadsheets" to "unscalable data layer" and triggers concepts like CRM or ERP.
3. Solution Mapping: Retrieves entities associated with "scaling database solutions."
4. Source Retrieval: Ranks sources based on their semantic proximity to the solution entities.

If your content is optimized solely for "best CRM," it may miss the reasoning chain entirely. Content optimized for the transition state, such as "migrating from sheets to CRM," has a higher probability of extraction because it semantically aligns with the model's derived intent.

Protocol: Building a Target Prompt List

We cannot rely on guesswork. We need a systematic approach to identify high-leverage queries.

1. Identify Trigger Scenarios (The Problem Layer)

Users often query LLMs during problem identification, not just solution selection.

• Keyword Approach: "Project Management Software"
• Prompt Approach: "How to synchronize engineering tickets between Jira and Linear?"

Actionable Step: Map the three critical technical or operational breaking points that occur immediately before a customer deploys your solution.

2. The Abstraction Hierarchy

Validating prompts requires testing across different levels of user intent.

• Problem-Aware (High Abstraction): "Why is my cloud ingest latency increasing?"
• Solution-Aware (Mid Abstraction): "Redis vs. Memcached for high-throughput caching."
• Product-Aware (Low Abstraction): "Does GenRankEngine support SSO?"

Visibility is often most valuable at the Problem-Aware layer where the model is actively synthesizing a recommendation set.

3. Operationalizing "Share of Model" (SoM)

Once candidate prompts are defined, we track Share of Model (SoM).

Definition: Share of Model is the percentage of randomized experimental runs where a specific brand or entity appears in the positive recommendation set of an LLM response.

Measurement Process:

1. Define a test set of 20 high-value prompts.
2. Run each prompt 5 times across GPT-4, Claude 3.5, and Gemini Pro. Ensure you clean context between runs.
3. Record success (brand mention), neutral (general advice), or failure (competitor mention).

Any prompt where the model successfully resolves the intent but recommends a competitor represents a Content Gap. This is a specific reasoning path your documentation or content has not yet satisfied.

Structuring Content for Extraction

To increase the probability of citation, content must be structured for machine readability. Models tend to extract high-density structured passages over narrative text.

Structure Requirements:

1. Entity Definitions: Use clear "X is Y" syntax.
2. Ordered Lists: Use numbered lists for steps or factors.
3. Data Density: Provide novel data points or benchmarks that do not exist in the model's training set.

Diagnostic Tooling

Manually testing hundreds of prompts across multiple models is resource-intensive.

GenRankEngine provides tooling to automate this loop. It allows engineering teams to batch-test prompt portfolios, visualize extraction rates across models, and identify specific content gaps causing visibility loss.

This data allows for precise iterations on content structure and semantic focus. It moves GEO from a guessing game to an engineering discipline.