The Triple Crown of AI Prompting: Building Systems for Human-AI Collaboration¶
Date: October 15, 2025 Author: Daniel Shanklin Tags: AI Strategy, Workflow Design, Knowledge Management
Executive Summary¶
Effective AI prompting requires more than describing desired features. The most successful AI implementations follow a three-part framework: clearly define what you currently have, what you want to achieve, and what trade-offs you cannot accept. This "Triple Crown" approach dramatically improves AI output quality by providing the constraint context AI systems need to generate practical solutions rather than theoretically optimal but unworkable approaches.
This framework also reveals a critical insight often overlooked in AI adoption discussions: organizations face a decade-long workforce transition where domain expertise and technical AI fluency rarely overlap initially. The experienced analyst with twenty years of industry knowledge often has limited exposure to AI-assisted workflows, while the technical team member comfortable with AI tools may lack deep domain expertise. Building systems that force all-or-nothing adoption creates artificial barriers. Instead, organizations need infrastructure that supports both traditional human authoring and AI-accelerated workflows, meeting team members where they are in their adoption journey.
Why Traditional Requirements Fail¶
Requirements gathering typically asks "what do you want?" This single question produces incomplete specifications because it focuses exclusively on desired outcomes while ignoring critical context about current capabilities and unacceptable constraints.
Consider a team selecting a documentation platform. The traditional approach generates a feature list: web-based editing, version control, search functionality, API access, collaboration tools. Every modern documentation platform checks these boxes. The list doesn't reveal which solution actually fits the organization's needs because it omits the context that makes requirements meaningful.
The Triple Crown framework adds two additional dimensions. First, what exists today that's worth preserving? The team already has MkDocs generating technical documentation, Claude Code enabling rapid AI-assisted article creation, and Git providing version history. Second, what changes would cause adoption failure? Forcing domain experts to learn Git workflows, requiring command-line tools for basic editing, or losing the ability to automate content creation with AI would all prevent successful implementation.
With this complete picture, the real requirement emerges clearly. The team doesn't need "a better documentation platform." They need a system that preserves AI automation capabilities while adding web-based editing for colleagues who prefer traditional authoring. This hybrid requirement only becomes visible when all three dimensions are explicit.
The Triple Crown Framework¶
The framework asks three questions in sequence, each building on the previous answer to create a complete requirements picture.
Start by documenting current capabilities honestly. What tools does the team already use effectively? What workflows produce good results today? What investments have already been made? This inventory reveals assets worth preserving and highlights where replacement would waste existing value. A team using Claude Code with MCPs to generate technical articles in minutes has built real capability. Abandoning that workflow to adopt a platform without API access destroys productivity unnecessarily.
Next, define desired outcomes specifically. What new capabilities would solve current problems? What limitations need addressing? What goals drive the improvement effort? Specificity matters here. "Better collaboration" remains vague. "Enable non-technical team members to edit articles through a web interface without learning Git" provides actionable direction.
Finally, identify constraints and dealbreakers explicitly. What trade-offs cannot be accepted? What disruptions would prevent adoption? What requirements, if violated, make the solution unworkable? These boundaries are often implicit in traditional requirements but making them explicit dramatically improves AI output quality. An AI system told "we need wiki functionality" might suggest any of dozens of platforms. That same AI told "we need wiki functionality but cannot lose API access for automated content creation and cannot force domain experts to use command-line tools" immediately narrows the solution space to practical options.
By providing all three dimensions, you enable AI systems to focus creative problem-solving within realistic boundaries rather than proposing theoretically optimal but practically unworkable solutions.
Case Study: Documentation Platform Selection¶
Our documentation team faced a common challenge. Claude Code with MCPs enabled rapid creation of high-quality technical articles, producing detailed analysis with code examples and diagrams in hours rather than days. However, this workflow required familiarity with development tools and AI prompting techniques. Experienced colleagues with deep domain expertise but limited technical backgrounds couldn't contribute, creating a bottleneck where valuable knowledge remained undocumented.
The current state included several assets worth preserving. MkDocs generated clean, searchable documentation sites from Markdown files. Git provided version control and change history. Claude Code automation produced consistent, well-structured articles following established style guidelines. The workflow worked excellently for technically-oriented team members but excluded others entirely.
The desired outcome was straightforward: enable all team members to contribute documentation regardless of their technical background or AI comfort level. Colleagues should be able to write and edit articles through a familiar interface without learning Git commands or AI prompting techniques. At the same time, technical team members who had mastered AI-assisted workflows should retain those capabilities rather than being constrained to slower manual processes.
The constraints revealed where solutions would fail. Forcing domain experts to learn Git workflows would prevent adoption—these colleagues needed to focus on sharing expertise, not mastering version control systems. Similarly, losing Claude Code automation would eliminate the productivity gains that made rapid documentation creation possible. Any solution requiring additional licensing outside the existing Microsoft ecosystem would face budget and approval barriers. Finally, self-hosted platforms requiring ongoing IT maintenance would consume resources better spent on content creation.
With these three dimensions explicit, the requirement became clear. The team needed a Microsoft-native platform supporting both web-based manual editing and API-driven programmatic content creation. SharePoint or Azure DevOps Wiki could serve as the common repository. Colleagues would edit through web browsers using familiar interfaces. Claude Code would write via REST APIs. Both authoring methods would create Markdown content stored in the same repository with identical formatting and structure. Collaboration features like comments and search would work identically regardless of how content was originally created.
This hybrid architecture emerged directly from the Triple Crown framework. Traditional requirements focusing only on desired features would have missed the critical constraint that both authoring modes must work seamlessly. A pure feature list might have suggested Confluence or Notion, platforms with excellent web editing but limited API capabilities for AI automation. The complete picture revealed that the real challenge wasn't finding a documentation platform—it was bridging the gap between AI-accelerated and traditional workflows in a way that respected both approaches.
The Workforce Transition Reality¶
Organizations implementing AI systems often overlook a fundamental truth about workforce adoption timelines. The transition to AI-assisted workflows will take years, not months, and different team members will adopt at different rates based on their roles, backgrounds, and comfort with new technologies.
Consider the typical organizational landscape. Experienced professionals who have spent decades building deep domain expertise often have limited exposure to AI tools and prompting techniques. These individuals contribute immense value through their knowledge and judgment, but asking them to simultaneously learn AI workflows while continuing to deliver in their primary roles creates unrealistic expectations. Meanwhile, technical team members comfortable with AI automation may lack the domain expertise necessary for content creation. Both groups have valuable contributions to make, but they work most effectively using different tools and approaches.
The pace of adoption follows predictable patterns. Early adopters, typically representing a small fraction of any organization, embrace new technologies quickly and find creative applications. These individuals become internal champions, demonstrating capabilities and building initial use cases. The early majority follows once tools mature and clear value becomes evident, but this group requires training, support, and time to develop competency. The late majority adopts when new approaches become standard practice, while some individuals never fully transition to new workflows.
This reality has direct implications for AI system design. Platforms that force all-or-nothing adoption create artificial barriers. The experienced analyst with twenty years of industry knowledge shouldn't be blocked from contributing because they prefer traditional editing interfaces. Forcing this individual to learn AI prompting and code-based workflows before they can document their expertise wastes valuable institutional knowledge. Similarly, the technical team member who has mastered AI-assisted content creation shouldn't be constrained to manual processes that eliminate their productivity advantages.
The path forward requires building infrastructure that supports multiple authoring approaches simultaneously. Allow the industry veteran to contribute through familiar web interfaces while the AI-fluent developer uses automated workflows to generate first drafts. Both approaches create content in the same system, following the same quality standards, appearing identical to readers. This hybrid model respects existing expertise while providing pathways for gradual capability development as team members become comfortable with new tools at their own pace.
Organizations that embrace this approach meet team members where they are rather than where technology enthusiasts think they should be. The result is broader participation, better knowledge capture, and natural adoption curves rather than forced transitions that create resistance and reduce engagement.
Designing Hybrid Authoring Systems¶
Hybrid authoring systems accommodate both traditional and AI-accelerated workflows without requiring users to choose between them. The architecture enables multiple input methods that produce consistent output through common validation and formatting layers.
Consider the basic architecture. AI authors use Claude Code with MCPs to generate content programmatically, calling APIs to create and update articles. Human authors use web-based interfaces with either WYSIWYG editors or simple Markdown editing. Both input methods connect to a common content repository where validation rules enforce style guidelines and formatting standards. The repository feeds a publishing platform where search, discovery, and collaboration features work identically regardless of content origin.
This architecture succeeds when several requirements are met. Multiple input methods must be genuinely parallel rather than having one primary approach with others as afterthoughts. Web interfaces need to be as capable as API-driven workflows for the tasks they support. Similarly, API automation shouldn't be constrained to capabilities available through the web UI. Each approach should play to its strengths—web editing for iterative refinement and collaboration, API automation for generating structured content following templates.
Content authored through any method should look and behave identically. Readers shouldn't be able to distinguish AI-generated articles from manually written ones based on formatting, structure, or quality. This requires style guides, templates, and validation rules that apply consistently regardless of input method. When an AI system generates an article via API, it should follow the same structure as a manually written piece. When a colleague edits content through the web interface, they should have access to the same formatting and organization tools.
Progressive disclosure becomes critical in these systems. Basic features must be immediately accessible to all users without training or technical prerequisites. An experienced professional should be able to create and edit content through the web interface within minutes of first exposure. Advanced capabilities like AI automation can be available when users are ready, but their absence shouldn't prevent basic participation. Training and documentation should scale to user experience levels, providing simple quick-start guides for beginners and detailed technical documentation for advanced users.
Platform flexibility ensures the system fits within existing organizational infrastructure rather than requiring wholesale change. Leveraging current Microsoft 365 licenses, using existing authentication systems, and integrating with tools teams already know reduces adoption friction. When the documentation platform feels like a natural extension of familiar tools rather than an entirely new system requiring separate learning, adoption accelerates naturally.
Implementation in Microsoft ecosystems might use Azure DevOps Wiki or SharePoint as the common platform. Claude Code writes articles by calling Microsoft Graph APIs or Azure DevOps REST endpoints. Colleagues edit through web browsers using built-in editors. Both approaches create Markdown content stored in Git repositories. Microsoft SSO handles authentication. Search and discovery use existing Microsoft 365 capabilities. The result is a unified system that feels native to the Microsoft environment while supporting both traditional and AI-accelerated authoring.
Using AI to Design the Solution¶
The approach demonstrates its own value recursively. We're using AI to determine how to build systems that don't require everyone to use AI—a practical illustration of the framework's flexibility.
The next phase of this analysis will use Claude agents to systematically research Microsoft-native platforms. SharePoint offers familiar interfaces and deep Microsoft 365 integration but has historically provided limited API capabilities for programmatic content creation. Azure DevOps Wiki provides strong API support and Git-based version control but may be less familiar to non-technical users. Microsoft Loop represents newer collaboration technology with promising capabilities but uncertain enterprise adoption and API maturity. OneNote offers universal familiarity but limited structured content organization.
The research will evaluate each platform against the Triple Crown requirements established earlier. Can Claude Code write articles programmatically through available APIs? Do web editing interfaces provide sufficient capability for manual authoring? Does the platform support technical content including code blocks, diagrams, and structured data? How does authentication and access control integrate with existing Microsoft 365 infrastructure? What migration path exists from the current MkDocs setup?
This systematic evaluation demonstrates the framework in action. By clearly defining what exists today (MkDocs with Claude Code automation), what we want to achieve (hybrid AI-human authoring), and what we cannot accept (forcing colleagues into technical workflows or losing automation), we enable focused research that evaluates practical options rather than exploring every theoretical possibility.
The research will provide implementation examples for the most promising platforms, including code samples showing how Claude Code would interact with platform APIs, screenshots of web editing interfaces for manual authoring, and migration strategies for transitioning existing content. This concrete analysis will support a well-informed decision based on actual capabilities rather than marketing claims or feature lists.
Broader Applications¶
The Triple Crown framework applies beyond documentation platform selection to any AI implementation challenge where context and constraints determine success.
Development teams designing AI-assisted coding workflows face similar requirements. They currently have functioning CI/CD pipelines, team members with established Git expertise, and existing development environments. They want AI-assisted code generation, automated testing capabilities, and faster development cycles. They cannot accept breaking current deployment processes, forcing wholesale tool migrations, or introducing AI systems that generate code without proper review and testing. The framework reveals that successful implementation requires augmentation of existing workflows rather than replacement, integrating AI capabilities into current development environments rather than demanding entirely new toolchains.
Business intelligence teams implementing natural language query systems face parallel challenges. They have legacy reports, SQL expertise among analysts, and existing dashboard systems. They want natural language query capabilities, AI-generated insights, and reduced manual report creation. They cannot accept replacing experienced analysts, losing report accuracy through automation errors, or introducing vendor lock-in with proprietary systems. The framework shows that the requirement is enhancing analyst productivity through AI assistance while preserving human oversight and leveraging existing expertise.
Customer support organizations automating response systems encounter the same pattern. They have experienced support teams, existing ticketing systems, and established quality standards. They want AI-assisted response drafting, faster resolution times, and improved consistency. They cannot accept fully automated responses without human review, reduced support quality through generic AI outputs, or systems that prevent escalation to human judgment when needed. The framework clarifies that success requires AI amplifying human capabilities rather than replacing human judgment.
In each case, the framework transforms vague improvement goals into concrete requirements that respect existing capabilities while enabling new functionality. The pattern repeats across domains: successful AI implementation augments rather than replaces human expertise, preserves rather than disrupts effective existing workflows, and provides progressive adoption paths rather than forcing all-or-nothing transitions.
Conclusion¶
Effective AI implementation starts with clear requirements, but "what do you want?" captures only one dimension of the challenge. The Triple Crown framework—what you have, what you want, what you don't want—provides the complete context AI systems need to generate practical solutions within realistic constraints.
This framework forces organizations to confront a fundamental truth about AI adoption that technology discussions often gloss over. The workforce transition to AI-assisted work will span years, not months. Team members will adopt new capabilities at different rates based on their backgrounds, roles, and comfort with emerging technologies. Both perspectives—the industry veteran with deep domain expertise who prefers traditional tools and the technically-fluent early adopter who has mastered AI automation—bring genuine value to the organization.
The best AI systems don't force adoption. They create infrastructure supporting multiple authoring approaches, enabling collaboration between AI-fluent and traditionally-skilled contributors. This hybrid approach respects existing expertise while providing pathways for gradual capability development. For organizations building documentation platforms, development workflows, or any system where both AI and human input have value, the question isn't "how do we force everyone to use AI?" Instead, ask "how do we build systems flexible enough to support team members wherever they are in their AI adoption journey?"
That challenge—and that opportunity—defines successful organizational AI implementation. The Triple Crown framework provides a structured approach to navigate these requirements by making implicit constraints explicit, enabling AI systems to focus on practical solutions rather than theoretical possibilities.
Coming next: We'll use Claude agents to research Microsoft-native wiki platforms and evaluate which best supports hybrid authoring, providing concrete recommendations for organizations facing similar challenges.