Pair-Authoring with an AI: A Case Study in Structured Collaboration
Moving beyond simple text generation to a collaborative partnership with a Large Language Model.
1. The Tempting Proposition: "Just Write the Presentation"
Every complex project begins with a familiar challenge: a folder full of raw material and a blank canvas. In my case, it was a collection of dense Google Docs, a GitHub repository, and a clear goal—to create a polished 15-minute presentation for an upcoming conference.
The temptation, of course, was to turn to a Large Language Model and use what I call a "lazy prompt": "Here are the documents, write me a 15-minute presentation."
But my past experiments in AI-assisted development have taught me a crucial lesson. This approach almost inevitably leads to what I've previously described as a
"structural mess" in a recent article Do developers need to think less with AI? The AI can generate content, but it cannot, on its own, generate a compelling structure from ambiguous source material.
Knowing this, I adopted a different role—not of a micromanager, but of a director and critic. My job wasn't to define every expectation in a vacuum. Instead, our process became a rapid, iterative loop: the AI would propose a draft, and I would review it, using my critique to constantly clarify and refine my expectations for the next generation, from the high-level outline down to the tone of a single sentence.
2. Two Principles for a Better Partnership
To avoid the disappointing results of lazy prompting, I applied a more disciplined approach based on two core principles. These principles weren't just about getting a better output; they were about creating a more effective and predictable collaborative process with the AI.
1. Define the "Pairing Contract" Upfront
Before generating any content, we established a clear "contract" that would govern our entire interaction. This wasn't a formal document, but a set of initial instructions that aligned the AI with my expectations. The key clauses of this contract were:
A Step-by-Step Workflow: We explicitly agreed to work in small, approved increments. The AI would propose a plan for a section or a draft for a slide, and I would approve or critique it before we moved on. This prevented the AI from getting too far ahead on a wrong path.
A Dual-Output Structure: For every slide, I specified two distinct deliverables: minimalist on-slide text for readability and a more detailed accompanying speech for the narrative. This separation of concerns is critical for creating effective presentations.
A Pre-Defined Tone: We established that the voice should be "professional and neutral," not "promotional." This simple directive guided the tone of every piece of generated text.
This upfront alignment ensured the AI's "creative freedom" was always channeled within the bounds of my strategic goals for the project.
2. A Lot of Tiny Steps
Borrowing a key principle from effective software development, I resisted the urge to ask the AI to solve large problems in one go. Instead, we broke down the task of creating the presentation into a hierarchy of tiny, manageable steps:
First, we debated and finalized the high-level outline of the entire presentation.
Next, we planned the narrative flow for one section at a time.
Then, we drafted the content for only one slide at a time.
Finally, once the core content was set, we refined the smallest details, like the wording of a single bullet point or the line breaks in a sentence for better visual balance.
This micro-iterative approach kept the AI's output focused and reviewable. It allowed for constant course correction and ensured that every component, from the overall structure down to the final polish, met my expectations before we considered it complete.
3. An Unexpected Discovery: The Power of Separated Concerns
In any deep-dive process, some of the most valuable insights are the ones you don't anticipate. While I expected the step-by-step workflow to be effective, I underestimated the impact of one particular clause from our initial "Pairing Contract": the strict separation of On-Slide Text and Accompanying Speech.
Initially, this seemed like a simple, practical rule for creating a presentation. However, it quickly revealed itself to be a powerful disciplinary tool that forced a higher level of clarity throughout the entire process. Here's how:
It Forced Conciseness. The most common failure mode of presentations is a slide cluttered with text. By having a dedicated place for the detailed narrative (the speech), we were forced to be ruthless with the on-slide text. The question was no longer "What should this slide say?" but "What is the absolute minimum text needed on this slide while I am speaking?" This led to cleaner, more impactful visuals.
It Clarified the Narrative. The dual-output structure forced us to constantly distinguish between the visual aid (the slide) and the story (the speech). At every step, we had to decide what the audience should see to anchor them, and what they should hear to understand the deeper context. This clarified the purpose of every single element we created.
It Improved the AI's Output. Giving the AI two smaller, distinct tasks consistently produced better results than one larger, ambiguous task. A prompt like "Write three concise bullet points for this slide" yielded more useful output than "Create content for a slide about Topic X."
What began as a simple formatting rule for a presentation revealed itself to be a core principle for effective AI collaboration in general. It is a powerful strategy for ensuring clarity and quality in any complex, multi-layered project. Ultimately, the AI didn't reduce my cognitive load; it shifted it. It took on the heavy lifting of drafting text, which freed me up—and required me—to focus entirely on the higher-level strategic thinking involved in shaping the narrative, structure, and tone.
Conclusion: Thinking Differently
This journey of creating a presentation with an AI partner confirmed a critical insight: the promise of these tools is not to reduce the need for human thought, but to change its nature. The most effective approach I've found combines the speed and pattern-recognition of the AI with the architectural thinking and quality standards that an experienced professional brings to the table.
Our success didn't come from a single, brilliant prompt. It came from a disciplined process: establishing a "pairing contract," taking a lot of tiny, deliberate steps, and enforcing a clear separation of concerns.
Ultimately, these tools are powerful amplifiers of our own capabilities, not replacements for our judgment. They excel at generating drafts, suggesting patterns, and handling the tactical work of filling in the details. However, they require a human director to provide thoughtful integration, strategic thinking, and a clear vision for the final product.
This isn't about thinking less—it's about learning to think more architecturally about the creative process itself. In my experience, the key to success is learning to direct these powerful tools effectively. As with any powerful tool, the final quality of the work lies not in the tool itself, but in the wisdom and discipline of its user.