Despite the advancements in AI, a SurveyMonkey study found that 31% of marketers have concerns about the accuracy or quality of AI tools. This isn’t just about minor typos; it points to deeper issues like inconsistent tone, factual errors, and a critical lack of contextual understanding – all of which derail global content efforts and inflate localization costs.
What if you could slash your AI-generated content costs in half without sacrificing quality or consistency? This isn’t just a hypothetical. In a recent episode of The Agile Localization Podcast, host Stefan Huyghe sits down with Steven Wiseman, Chief Revenue Officer and Co-Owner at WritePoint, to unpack a framework that promises exactly that.
Dubbed the Content Excellence Framework, this method helps teams dramatically improve the quality of AI-generated content and reduce localization costs across the board.
From structured blueprints to smart verification loops, Steven’s approach isn’t about chasing AI hype; it’s about operationalizing AI content creation with clarity, control, and real business value.
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The Problem with AI Content Today
AI content often falls short because we’re giving it vague prompts and expecting magic. The biggest issue? Lack of context and consistency. AI is like a super-intelligent employee who can deliver incredible results, but only if you provide clear instructions, backgrounds, and structure.
Many teams focus exclusively on prompt engineering, which Steven argues is just one small piece of the puzzle. Without structured input, a defined style, and a guiding framework, you end up with inconsistent outputs that require heavy human editing, especially when translating content into multiple languages.
Enter the Content Excellence Framework
To solve this, Steven introduces the Content Excellence Framework, built around four practical pillars:
- Content Blueprints
- Source Material Organization
- Personas
- Verification Loops
Let’s break each one down.
1. Content Blueprints: Your AI’s Instruction Manual
At the heart of the framework is the content blueprint, a less intimidating term for what’s traditionally known as an “information model”. Instead of overwhelming teams with 100-page documents, a content blueprint provides a practical, scalable way to guide content creation.
Think of it as an advanced style guide on steroids. It covers:
- Types of documents you’re creating
- Preferred structure (e.g., standard table of contents)
- Sentence style, tone, voice
- Localization considerations (e.g., short sentences, graphics with callouts)
- Terminology and glossary usage
Even a two-page blueprint can significantly improve AI outputs, and it’s a living document that evolves with each project.
2. Organized Source Material: Fuel for High-Quality Output
Next, Steven highlights the importance of gathering and organizing all available source content: product guides, Kira tickets, meeting transcripts, and even emails. Instead of letting valuable information scatter across tools, bring it together into an accessible, structured source file.
AI thrives when fed with rich, organized material. Often overlooked due to casual “how are you” chatter, transcripts can contain gold nuggets of user context and product insights. Feeding these into your LLM ensures more accurate and relevant output.
3. Personas: Train AI Like It’s Joining Your Team
AI isn’t just generating content; it’s taking on a role. Steven suggests building detailed personas for both the AI itself and its target audience.
- AI Persona: Define what kind of expert the AI should act as: technical writer, compliance expert, multilingual translator.
- Customer Persona: Specify the needs, expectations, and tone appropriate for your audience. Different personas may apply across markets, product tiers, or departments.
4. Verification: Don’t Just Trust — Verify
Here’s where it gets clever. Before sending AI-generated content off for human editing or translation, run it through a second AI project using the same blueprint and persona. This acts like a peer review. It flags inconsistencies, formatting issues, and off-brand language before they snowball into costly revisions, especially helpful in multilingual workflows where translation errors can amplify inconsistencies.
Why It Matters for Localization
Too often, localization is treated as an afterthought, a costly, last-minute process that eats up time and budget. But as Steven puts it, “If somebody doesn’t prepare for multilingual, they’re going to pay for it later.” By making localization considerations into the original content blueprint, teams can create AI-generated content that’s already optimized for translation:
- Clear structure
- Simplified language
- Consistent terminology
- Minimal idioms or culture-specific references
Can You Operationalize This? Yes. Here’s How
Frameworks are great in theory, but how do you get buy-in from your team? Steven offers a practical rollout strategy:
- Start Small: Create a simple, 1-2 page blueprint for a pilot project.
- Show the “Wow” Factor: Compare content created with and without the framework. Let the difference speak for itself.
- Phase Your Rollout: Begin with one team or content type, then scale up.
- Let AI Help You Build the Blueprint: Use your LLM to suggest what’s missing from the blueprint and update it dynamically as you go.
And if you’re using platforms like Crowdin? The framework fits right in. Steven even suggests a future where localization tools include blueprint verification as a built-in feature.
Tip: Crowdin doesn’t just host your content; it provides the robust features needed to implement Steven’s framework at scale:
- Content Blueprints: Leverage Crowdin’s Style Guides and References App to define your AI’s instruction manual, ensuring consistent tone, style, and localization considerations from the start.
- Organized Source Material: Centralize all your valuable knowledge – from TMs to glossaries – within Crowdin. This rich, organized data fuels high-quality AI outputs and minimizes rework (learn more about Crowdin’s features).
- Verification Loops: Implement sophisticated AI-powered QA checks within Crowdin to proactively identify inconsistencies and errors in AI-generated content before it reaches human translators, drastically cutting down on revision costs.
Ready to integrate the Content Excellence Framework with your Crowdin workflow?
Final Thoughts From Steven
While the promise of a 5-% cost reduction is headline-grabbing, the real win here is quality. Steven’s framework elevates content from passable to professional, making AI a real partner in content creation, not just a shortcut. With structured input, clear blueprints, and smart verification, AI content creation becomes faster, cheaper, and better, not just in English but in every language your business speaks. So whether you’re in localization, documentation, or content ops, it might be time to stop wrestling with prompts and start building a framework.
Steven’s Background
Steven Wiseman is the CRO and Co-Owner at WritePoint, bringing over three decades of expertise in documentation and content management. With a background spanning technical documentation and entrepreneurship, he has established himself as a thought leader in content excellence frameworks. Steven’s practical framework for structured content has demonstrated significant improvements in content creation efficiency, reducing authoring time by 50-60%.
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