A recent survey of computer science publications found that 73% of papers published in 2024 included code examples generated or modified by AI systems. That number was 12% just two years ago. For those of us building bots and automation systems, this shift isn’t abstract—it’s showing up in our daily workflow.
I’ve been building conversational AI for six years. My process used to look like this: sketch the bot’s logic on paper, write the code, test it, document it, then write about it. Each step was distinct. Now? The boundaries between these steps are blurring in ways that feel both useful and unsettling.
When Your Tools Start Writing Your Tutorials
Last month, I was building a sentiment analysis module for a customer service bot. I asked an AI assistant to generate the initial classification logic. It produced working code in seconds. Then I asked it to explain the code for a tutorial I was writing. It generated clear documentation with examples.
Here’s what struck me: the explanation was better than what I would have written. Not because I lack the knowledge, but because the AI had infinite patience for covering edge cases and alternative approaches. It didn’t get tired or want to skip the boring parts.
This creates a strange loop. I’m teaching people to build bots using explanations generated by bots. The code works. The tutorials are clear. But something feels different about the authorship.
The Measurement Problem
In scientific research, there’s a concept called “reproducibility”—can someone else follow your methods and get the same results? With AI-generated code, this gets complicated.
If I write a tutorial that says “use this prompt to generate your webhook handler,” different readers will get different code. The logic might be similar, but the implementation details will vary. Version control becomes fuzzy. Debugging becomes collaborative between human and machine in ways our tools weren’t designed for.
I’ve started keeping what I call “generation logs”—records of the prompts I used, the model version, and the timestamp. It feels like lab notes. My GitHub repos now have a new section: “AI Generation Context.” Other bot builders are doing similar things, inventing practices as we go.
What This Means for Bot Architecture
The practical impact on how we build systems is significant. I’m seeing three major shifts:
- Faster prototyping but slower validation—generating code is quick, but verifying it works correctly across edge cases takes longer than before
- Documentation that evolves—instead of writing docs once, I’m maintaining prompts that generate current documentation on demand
- Collaborative debugging—when code breaks, figuring out whether the issue is in my logic, the AI’s generation, or the interaction between them requires new diagnostic approaches
The Craft Question
There’s a deeper question here about skill development. When I teach workshops on bot building, I used to focus on teaching people to write code from scratch. Now I’m teaching them to evaluate, modify, and integrate AI-generated code. These are different skills.
Is one better than the other? I don’t think so. But they’re not the same. A chef who can taste a dish and adjust the seasoning has different expertise than one who can create a recipe from scratch. Both are valuable. Both require knowledge and judgment.
The challenge is that we’re still figuring out what “good judgment” means in this context. When should you accept AI-generated code as-is? When should you rewrite it? How do you know if you’re becoming dependent on generation versus using it as a tool?
I don’t have clean answers. What I do have is a growing collection of practices that seem to work: always read generated code line by line, test edge cases manually, keep humans in the loop for architecture decisions, and document not just what the code does but why you chose to generate it this way.
The tools are changing faster than our practices can adapt. That’s uncomfortable. It’s also the most interesting time I’ve experienced in this field.
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