Working Effectively with Claude: From Vibe Prompting to Context Engineering for Technical Content

How to leverage AI as a collaborative tool for creating educational content that actually works

In my recent post about The Mythical Vibe-Month, I wrote about how "vibe coding" (aka throwing prompts at LLMs and hoping for magic) creates fragile, context-free outputs. But here's what I didn't mention: the same principle applies to content creation.

Over the past several months, I've been working with Claude to create technical documentation, blog posts, tutorials, and educational materials. Not through vibe prompting, but through what I call collaborative context engineering: treating Claude as a learning partner rather than a magic answer machine.

The difference? Instead of expecting Claude to be the "sage on the stage" delivering perfect content from thin air, I position myself as the "guide on the side," directing our collaboration toward better outcomes. This mirrors how effective learning actually works: through dialogue, iteration, and building understanding together.

The Problem with Vibe Prompting for Content

Most people approach AI content creation like this:

"Write a blog post about document processing for developers"

And then they're surprised when the output feels generic, misses crucial context, or doesn't match their voice. It's the content equivalent of landing in Knockturn Alley instead of Diagon Alley; close enough to feel right, but missing the mark entirely.

The problem isn't the AI's capability. The problem is that good technical content requires lived context that can't be vibed into existence:

• The specific pain points your audience faces
• The mental models that help concepts click
• The edge cases and gotchas from real implementation
• Your unique perspective and voice
• The broader strategic context of why this content matters

Context Engineering for Content Creation

Instead of hoping Claude vibes its way to good content, I treat our collaboration as a context engineering exercise. Here's my actual process:

1. Start with Research, Not Writing

When I want Claude to help with content, I never start with "write this for me." I start with "learn about this with me."

My typical opening prompt:

"I want you to do research on [topic] and learn about the challenges facing my ICP by asking me questions, if needed. You can also find information by search competitors such as X, Y, or Z, referencing previously published work [links] and reading through docs [links]. You should also do research on me to better understand how I would position and explain this topic. You can find information about me on my GitHub, LinkedIn, and resume. You should also read these blog posts to understand my tone [links]."

This isn't delegation; it's collaboration setup. I'm giving Claude the tools to understand both the subject matter and my perspective. Claude then researches, asks follow-up questions, and builds context before we ever touch content creation.

Why this works: Claude becomes a learning partner who understands my voice, my audience, and my goals, rather than a content generator working from assumptions.

2. Guide Through Questions, Don't Dictate Answers

After Claude does initial research, I don't immediately jump to "now write the thing." Instead, I let Claude ask me questions...lots of them.

In our collaborations, Claude typically asks things like:

• "What are the biggest barriers you've observed that prevent engineers from achieving what they want with code?"
• "What does success look like for you in this role?"
• "How do you see the thread between [your previous work] and [current work]?"

These aren't just information-gathering questions. They're the kinds of questions that help me clarify my own thinking. Often, Claude's questions surface insights I hadn't explicitly articulated, even to myself.

Why this works: Good content comes from clear thinking. The Socratic method Claude uses here forces me to crystallize ideas that might have been fuzzy, giving us both better material to work with.

3. Iterate Through Feedback, Not Replacement

When Claude produces content, I never treat the first draft as the final product. Instead, I provide specific feedback:

• "This section needs more technical depth"
• "The tone here doesn't match my voice from [previous article]"
• "Add a concrete example of [specific scenario]"
• "This analogy doesn't quite work for our audience"

Crucially, I don't just say "make it better." I give Claude the context it needs to improve in the right direction.

Why this works: Each iteration teaches Claude more about my standards, voice, and audience. The content gets progressively better, and Claude gets progressively better at anticipating what I need.

4. Supplement with Additional Research

Mid-conversation, I often ask Claude to research additional context:

• "Look up the latest developments in [technology area]"
• "Find examples of how [specific company] approaches this problem"
• "Research the best practices around [implementation detail]"

This isn't because Claude's initial research was insufficient. It's because good content creation is an exploratory process. As we develop ideas together, new research needs emerge.

Why this works: Real-time research keeps our content current and comprehensive. It also models how I actually write: constantly fact-checking, finding examples, and building on new information.

What I Actually Publish vs. What Claude Produces

Here's something crucial: I never publish Claude's output directly. What Claude produces is sophisticated first-draft material that captures my voice and ideas, but it's not my final work.

My published content goes through several more layers:

• Structural editing where I reorganize for better flow
• Voice refinement where I adjust tone and style to exactly match my perspective
• Technical validation where I verify every code example and technical claim
• Audience optimization where I add specific details that resonate with my community

Think of Claude as an extremely capable research assistant and thought partner, not a ghostwriter. The ideas, insights, and expertise are mine. Claude helps me organize and articulate them more effectively.

Why This Matters for Engineers Building with AI

If you're working on agentic applications, LLM integrations, or AI-powered developer tools, this collaborative approach offers several lessons:

Context is everything. The most sophisticated AI in the world can't substitute for domain knowledge, user empathy, and situational awareness. Your job isn't to automate human expertise away, it's to amplify it.

Iteration beats generation. Instead of building systems that try to produce perfect outputs in one shot, build systems that support rapid iteration and refinement. The magic happens in the feedback loop, not the initial output.

AI should teach, not just execute. The best AI tools I've used don't just perform tasks, they help me understand problems better, ask better questions, and develop better solutions. Claude's questioning approach has genuinely improved my thinking about content strategy and audience needs.

Practical Takeaways

Whether you're creating documentation, building developer education, or working on any AI-powered content creation:

1. Set up context before requesting output. Give your AI tool the background information it needs to understand your goals, audience, and constraints.

2. Use AI to help you think, not just to produce. The best prompts are often questions that help clarify your own thinking.

3. Plan for iteration. Your first output won't be your final output. Build feedback loops into your workflow.

4. Maintain human judgment. AI can help you articulate ideas and organize thoughts, but the expertise, creativity, and final decisions should remain yours.

The Meta-Point About Tools

This brings me back to the core insight about working with any powerful tool: when you have a hammer, not everything is a nail. Claude is incredibly capable, but knowing when and how to use it effectively makes all the difference.

The goal isn't to automate content creation. The goal is to amplify human expertise, accelerate the iteration cycle, and create better outcomes through collaboration.

In my work, this approach has helped me create documentation that developers actually use, tutorials that successfully onboard new users, and content that genuinely advances our mission of making document AI accessible to engineers at any level.

That's the difference between vibe prompting and context engineering. One hopes for magic. The other creates it, systematically and sustainably.

Because at the end of the day, the best tools don't replace human expertise, they make that expertise more powerful.