I recently came across a post on LinkedIn about how PIQPACC reshaped someone’s approach to strategic planning—and it immediately clicked.

Over the last few years, I’ve been hands-on with AI-assisted knowledge management: piloting GenAI to support customer-facing documentation, tuning chatbots, optimizing search, and leading Knowledge-Centered Service (KCS) programs at enterprise scale.

Looking back, some of the lessons I learned align perfectly with the PIQPACC framework, even though I didn’t know the name for it at the time.

A real-world GenAI lesson

During one pilot, we explored using generative AI to create and improve customer-facing product knowledge base articles, with the goal of reducing manual authoring and scaling faster documentation.

At first, the output looked great: clear steps, solid structure, confident tone.

But on closer inspection:

• The model started strong, then drifted into inaccurate steps—likely pulling from outdated product versions or similar tools with overlapping terminology.
• In some cases, and in some organizations, this kind of drift could surface internal or customer-specific information that slips into the model’s context or retrieved data. It’s not just a content issue—it’s a serious data governance risk.

The tooling wasn’t broken. The process was. We’d underestimated how GenAI interprets context, how retrieval systems surface data, and how easily content can go off-track without human oversight and structure.

That’s when PIQPACC would’ve made all the difference.

PIQPACC in hindsight:

• Purpose – Were we solving the right content challenge? Who was the real audience?
• Information – Was our training or retrieval data governed, current, and safe to use?
• Questions – What were we not asking before releasing GenAI-assisted content?
• Perspectives – What would a support agent, legal reviewer, or customer think of this output?
• Assumptions – Did we assume the model “knew” our product or audience?
• Concepts – Did everyone involved understand prompt behavior, token limits, or hallucination risk?
• Conclusions – Were we drawing conclusions from a flashy demo—or a validated, scalable process?

Why PIQPACC matters now more than ever

As GenAI becomes deeply embedded in enterprise workflows—from support content and search to chatbots and customer-facing tools—the cost of uncritical thinking grows fast:

❌ Inaccurate guidance
❌ Sensitive data exposure
❌ Erosion of trust in your support channels

PIQPACC gives you a framework to stop these issues before they start. It brings structure, cross-functional awareness, and a shared language for AI design and deployment.

For me, it retroactively explained so many of the challenges I encountered, especially in high-stakes environments where documentation accuracy and customer trust are non-negotiable.

The Smarter Path Forward

If you’re leading GenAI initiatives in content, knowledge, or customer experience:
Don’t just build smarter prompts. Build a smarter process.

PIQPACC didn’t invent critical thinking, but it gave me a framework for the questions I was already asking in the trenches. It turns instinct into intention, and isolated caution into repeatable practice. In a space moving as fast as GenAI, that kind of clarity isn’t just helpful—it’s essential.

Thanks to Becki Saltzman for shaping and sharing a framework that meets the moment.

Think before you prompt. Then build boldly—with clarity, context, and care.

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🔍 Interested in how PIQPACC applies to chatbot tuning, GenAI search workflows, or self-service content strategy? I’m always happy to connect and swap lessons learned.