Manual Scripting vs. Prompt-Based AI Automation — Which One Wins?

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Hey everyone :waving_hand:

I’ve been knee-deep in automation lately, and one thing keeps popping up:

Do we still need to hand-code every step, or is prompt-based AI the new standard?

Let me explain:

:wrench: Manual Scripting Pros:

  • Full control over logic, timing, retries
  • Lightweight and fast
  • Great for repetitive actions across known interfaces

:-1: Cons:

  • XPath or selector maintenance is hell :firecracker:
  • Easily breaks with UI changes
  • Hard to scale across multiple websites

:robot: Prompt-Based AI Scripts (like using GPT with Hidemium):

  • You describe the goal in plain language:

“Go to Instagram, log in, follow account A, then like the latest post.”

  • The AI figures out the DOM interaction
  • Less fragile — works even when the button class changes
  • More readable, even for non-coders

Of course, AI scripts can be slower and less precise — but they feel like the future for rapid prototyping or operating across 10+ websites.

:brain: What I’d love to know from the community:

  • Do you use Prompt Script AI with Hidemium or still prefer full code?
  • Have you benchmarked execution time or approval rate differences?
  • Would you ever combine both? (e.g. AI for navigation + hard code for critical steps)

Let’s trade notes — this could shape how we all build automation in 2025 :rocket:

2 Likes
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I tried writing some scripts using prompt on Hidemium, and was quite surprised that it understood even the most vague instructions. However, it still sometimes fails if the web page loads too slowly or has captchas.

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I’ve been mixing both — use AI scripting for general navigation, then drop to manual JS for precise clicks or when timing really matters. Best of both worlds.

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Well,

depends on the task. For smaller "scrapes"it works okayish, for bigger “crawls” I avoid using AI, way too slow. Example we crawl 2.4Mio Domains daily and need to parse between 600 to 1mio pages a day.

There´s no AI that can handle this parsing, let alone the costs. For general parsing we use Apache Tikka and for specific data extractions NLP/ML Pipelines, which actually do a better job at this scale. A weid thing is hallucinations when you do this at scale, the first couple hundreds thousands datasets get parsed really well and out of a sudden it invents values, which are not even in the dataset.

This happens across all major LLMs.