Large language models (LLMs) such as ChatGPT and Claude are changing how digital workflows are executed. Tasks like content generation, keyword research, technical analysis, and site diagnostics can increasingly be performed through LLM-driven interfaces.

The Prompts feature fits into this shift by acting as a controlled infrastructure layer between internal system data and external LLMs. Instead of asking an LLM a standalone question with limited context, the system sends structured prompts enriched with relevant business, website, and SEO data.

What the Prompts Feature Is

The Prompt Snippet is a prompt execution infrastructure that connects system data with external LLMs.

It serves as a bridge between:

• Internal structured data, such as domains, sitemaps, keywords, target audience data, pages, topical authority data, and business information

• External LLMs, such as ChatGPT and Claude

The feature exists because standalone LLM usage is often limited by incomplete context. A user may write a strong prompt, but without structured site and business data, the output may be generic, incomplete, or inaccurate. The source material emphasizes that LLMs require relevant context and that without it, they may “hallucinate” or produce inaccurate results .

Core Components

Predefined Prompts

The system includes a default library of prompts for common workflows, such as target audience analysis, keyword research, content creation, content planning, topical authority planning, and robots.txt analysis.

Custom Prompts

Users can edit existing prompts or create their own. Prompts can be customized, saved, and reused. Users can also mark preferred prompts as favorites for easier access.

LLM Integrations

The feature supports multiple LLMs, including ChatGPT and Claude. Users can choose which model to invoke depending on the task.

Different LLMs have different constraints. For example, some allow data to be passed through a URL, while Claude may require data to be passed through the desktop client instead .

Placeholder Injection

Placeholders dynamically insert structured data into prompts. Examples include:

• Website domain

• Sitemap

• Business name

• Target audience

• Keywords

• Brand information

• Product information

• Page data

• Topical authority data

This allows the same prompt template to produce different outputs depending on the selected site, audience, topic, or dataset.

External and Internal Data Blending

The system can combine internal platform data with user-configured data libraries. These libraries may include audience profiles, product information, business details, or other reusable context.

How It Works

1. The user selects a prompt from the prompt library or chooses a custom prompt.

2. The user selects an LLM, such as ChatGPT or Claude.

3. The user configures placeholders to inject structured data.

4. The system aggregates the prompt and data into a format the selected LLM can process.

5. The prompt is executed in the LLM.

6. The user receives the output and applies it to the workflow.

For example, a keyword research prompt can include both website data and a previously generated target audience file. This allows the LLM to perform keyword research based on the actual site context and intended audience, rather than a generic keyword request.

Key Capabilities

Data-Enriched Prompting

The main capability is context injection. The system passes structured data to the LLM so the model can generate more relevant and accurate outputs.

Multi-LLM Flexibility

Users are not locked into one model. The architecture allows prompts to be run through different LLMs as model capabilities change.

Reusable Prompt Templates

Prompts can be reused across tasks, edited, saved, and favorited.

Integration with Business Data Libraries

Users can maintain reusable data libraries, such as target audience definitions, product details, or brand information, and inject them into multiple prompts.

Improved Output Quality

The system improves output quality by giving the LLM more structured context. The source material states that outputs generated with system data are more useful than responses produced from limited standalone prompting .

Advanced Functionality

Prompt Outputs Connected to Actions

Prompt outputs can be connected to follow-up actions. Examples include:

• Publishing generated content to WordPress

• Updating a CMS

• Creating metadata

• Updating robots.txt

• Adding generated keywords back into the system

• Producing schema or JSON-LD outputs

Agent and Automation Potential

The architecture is designed to support agent-based workflows. After the LLM produces output, an agent can potentially execute the next step, such as updating a CMS or applying metadata.

MCP-Style Return Path

The source material describes a workflow where data generated by the LLM can be taken back into the system and applied, such as loading optimized meta tags into an account or website through the system’s implementation layer .

Open Architecture and Customization

The feature is designed as an open, flexible architecture. Users can customize prompts, connect different LLMs, and adapt workflows for different business contexts.

Specialized Skills and Instruction Layers

The system can use specialized “skills” or instruction layers, especially with Claude, to guide the LLM for domain-specific tasks such as robots.txt analysis.

Example Use Cases

Target Audience Analysis

The system can analyze a website and generate a target audience profile. That output can then be reused in later prompts.

Content Generation

Users can generate long-form articles using website data, keyword data, target audience data, and business context.

Keyword Research

A keyword research prompt can use injected site data and target audience information to produce more relevant keyword suggestions.

Technical SEO Checks

The system can analyze robots.txt files and provide recommended configurations.

Content Planning

Users can generate content plans, including multi-article plans, using topical authority data, target audience data, and website context.

Metadata and Schema Generation

Prompts can be used to generate optimized meta tags, schema, or JSON-LD outputs for pages.

Limitations and Considerations

The feature depends on the capabilities and constraints of external LLMs. Each LLM may handle data passing differently.

Structured data quality is also critical. The better the input context, the better the likely output.

The source material describes the feature as early-stage or “semi-beta,” meaning users should expect ongoing refinement and potential issues during implementation .

Best Practices

• Use rich, structured context whenever possible.

• Use placeholders to inject relevant data instead of manually rewriting prompts.

• Customize default prompts for specific workflows.

• Combine multiple data sources, such as website data, target audience data, keywords, and product information.

• Reuse outputs from one prompt as inputs for another prompt.

• Select the LLM based on the task and the model’s data-passing capabilities.

The Prompt Snippet provides a structured way to use external LLMs inside business and SEO workflows.

Its core value is not simply sending prompts to an LLM. Its function is to combine reusable prompt templates with structured system data, user-defined context, and LLM integrations.

This makes LLM execution more controlled, reusable, and context-aware than standalone prompting.