12 Days of AI Use Cases Day 1: Documentation Knowledge Blocks

Welcome to the 12 Days of AI Use Cases, 2025 Edition!

In this series, we’ll be looking at different use cases for AI – in particular, generative AI and large language models, the software that powers tools like ChatGPT, Google Gemini, and Anthropic Claude. Each day, we’ll look at the use case through the lens of the Trust Insights 5P Framework to see the role AI plays in achieving real, tangible outcomes.

We designed these use cases not only for reading but also as context you provide to generative AI tools as part of a prompt to help you achieve the outcomes you’re after. Ask the generative AI tool of your choice to help you implement this use case and copy/paste it in as part of the Trust Insights RACE AI prompt framework – this goes in the Context portion of our prompt framework.

Let’s dig in!

Purpose

Marketing and analytics professionals need documentation that addresses their specific operational challenges, yet standard platform documentation often fails to provide contextually relevant guidance. Deep research capabilities in generative AI platforms enable teams to create custom documentation knowledge blocks—comprehensive, pre-baked context that informs future AI interactions and provides step-by-step guidance for complex tasks. Teams deploy these knowledge blocks as reusable assets across multiple conversations, ensuring consistent execution and reducing the time teams spend searching for fragmented information. This use case demonstrates how teams can leverage AI-powered deep research agents to synthesize scattered information into actionable manuals, requirement documents, and process guides tailored to their unique workflows.

People

The primary actors performing this use case include marketing operations professionals, data analysts, and technical marketers who need clear documentation for platform features, analytical processes, or technical implementations. These professionals typically work within tools like Google Analytics 4, marketing automation platforms, or data visualization software, and they require step-by-step guidance that reflects their specific use cases rather than generic platform documentation.

Internal stakeholders who benefit from this use case include team members who will reference this documentation, managers who need to ensure consistent processes across their teams, and new hires who require onboarding materials. The existence of these knowledge blocks reduces training time, minimizes errors that inconsistent execution introduces, and establishes institutional knowledge that persists beyond individual team members.

External audiences impacted by this use case include executives and stakeholders who receive reports or dashboards that teams build using the documented processes. When teams follow clear, well-documented procedures, they produce more consistent and reliable outputs, which strengthens stakeholder confidence in the data and insights they receive.

Process

1. Define the specific operational challenge or task that requires documentation—for example, building a social media performance dashboard in Google Analytics 4’s Explore Hub, writing a Python script for data transformation, or establishing best practices for a mobile app implementation.
2. Craft a detailed prompt for the deep research tool that includes the task objective, the intended audience for the documentation, the stakeholder requirements, and the desired format (step-by-step manual, requirements document, best practices guide, or technical specification).
3. Submit the prompt to a deep research agent in platforms such as ChatGPT, Google Gemini, Anthropic Claude, DeepSeek, or similar tools that offer research synthesis capabilities—ensuring you use the research mode rather than standard chat interfaces to minimize hallucinations and maximize accuracy.
4. Review the generated documentation for accuracy, completeness, and relevance to your specific context—verify that the knowledge block addresses your operational requirements and includes the level of detail your team needs to execute the task successfully.
5. Store the completed knowledge block in a shared document repository such as Google Drive, OneDrive, or your organization’s knowledge management system, making it accessible for future AI conversations and team reference.
6. Deploy the knowledge block in subsequent AI interactions by uploading the document or copying its contents into conversation contexts, allowing the AI to reference the established procedures when analyzing new data, building reports, or solving related challenges.
7. Update the knowledge block periodically as platforms change features, as your team discovers process improvements, or as organizational requirements evolve, treating these documents as living assets rather than static references.

Platform

• Generative AI platforms with deep research capabilities: ChatGPT (with Deep Research or web browsing enabled), Google Gemini Advanced, Anthropic Claude with web search, DeepSeek, or Alibaba’s Qwen models
• Document storage and collaboration systems: Accessible document storage systems for your organization such as Google Drive, Microsoft OneDrive, Notion, Confluence, or SharePoint for storing and sharing completed knowledge blocks
• Analytics and marketing platforms requiring documentation: Google Analytics 4, Adobe Analytics, marketing automation platforms, social media management tools, data visualization software, or custom applications
• Required input data: Specific task objectives, stakeholder requirements, current process pain points, platform feature lists, and examples of desired outputs
• Integration points: Document management APIs for programmatic retrieval of knowledge blocks in AI conversations, version control systems for tracking documentation updates

Performance

We successfully implement this use case when teams consistently reference custom documentation knowledge blocks instead of searching for fragmented information across multiple sources, when new team members onboard more rapidly using these tailored guides, and when the quality and consistency of analytical outputs improve because professionals follow standardized, well-documented procedures. The knowledge blocks reduce repeated research time and establish institutional knowledge that persists as team composition changes.

1. Documentation reference frequency: Measure the frequency with which team members access and reference the created knowledge blocks in their workflows, targeting a usage rate where team members consult each knowledge block at least 5-10 times per quarter, demonstrating that the documentation provides ongoing value beyond its initial creation.
2. Time reduction in task execution: Track the decrease in time required to complete documented tasks after teams create knowledge blocks, comparing baseline execution time to post-documentation time, with a target reduction of 30-50% for complex multi-step processes.
3. Process consistency score: Evaluate the standardization of outputs across team members performing the same documented task, measuring the reduction in variation or errors, with a goal of achieving 90%+ consistency in following documented procedures within three months of implementation.

We hope this use case is clear and helpful. If you’d like help implementing it or any other AI use case, reach out and let us know.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.