Why does fixing AI mistakes take longer than doing it myself?
AI tools generate output so quickly that reviewing and correcting their errors requires hours of meticulous human work. This constant debugging in complex tasks and basic data summaries negates the intended time savings and prevents professionals from relying on the automation.
What people tried
Every workaround mentioned in the threads below. We haven’t tested any of them — and nobody here is claiming they worked.
- 1manually reviewing and checking actual results
- 2using AI only for basic general summaries
- 3bringing back human staff to review outputs
- 4Doing the calculations manually
- 5Double-checking AI outputs or restricting usage to senior staff
- 6Contacting local news organizations or attorneys to report defamation
- 7Filing feedback reports directly with tech platforms to eventually stop the hallucinations
- 8Tightening trust signals, reviews, and cross-platform consistency to change the model's pattern matching
- 9Venting frustration to coworkers
- 10Switching between multiple third-party companies
- 11Specifying strict AI policies and allowed uses directly in contracts
- 12Sending unedited work back to make vendors justify their billed hours
- 13Spending twice as long manually checking and reviewing AI output
- 14Limiting AI use strictly to basic knowledge assistance or general file searching
- 15Building custom templates and FAQ inputs to guide the models
- 16Stepping in and fixing the code manually after spending hours trying to get the AI to do it
- 17Taking time to understand the generated code during AI-assisted work to be able to debug and fix it later
- 18refusing to give out technical information or pretending not to know anything
- 19fixing what can be fixed and leaving strange parts as they are
- 20doing the code review and taking care of the production transition despite feeling unvalued
- 21Using external tools like Claude or ChatGPT via APIs, custom scripts, or Model Context Protocols (MCP) instead of native platform AI features
- 22Writing custom-coded actions using enterprise-tier bring-your-own-LLM API keys
- 23Doing the research and workflow building manually rather than relying on native AI assistants
- 24Recreating the documents from scratch while pretending the AI draft makes sense.
- 25Limiting AI usage to deterministic, repeatable tasks with clear inputs and predictable outputs
- 26Asking whether the tool is actually replacing work or just generating more reading material
In their words
Unedited, most upvoted first, each linked to the thread it came from.
“It produces so much, it takes humans weeks to find the problems created in an hour long session.”source ↗
“At that moment, I thought about telling him that I would think about it, but I realized that this was just a politically correct way of telling me that they had changed the site's code and, more importantly, that they no longer really needed me.”source ↗
“So I told him that I understood, but that today I have no context, no explanation. I can see the code and I can quickly see the feature, but I have no brief and no explanation for certain things that remain strange.”source ↗
“He dared to give me the Markdown files that Claude Code used to write the code, which are files already generated by Claude Code.”source ↗
“If it’s not 100% right, I’d end up spending just as much time finding the error especially in complex workings.”source ↗
“Gemini is defaming and lying about my business. How do you even combat this?”source ↗
“So yesterday someone commented on one of my ads that Gemini said my website is a scam, so I tried it, and guess what, it did straight up say my business is a scam based on customer feedback that it literally 100% invented and hallucinated.”source ↗
“Kind of shocked. What the hell are we paying for? He literally didn’t even proofread the AI results he sent us.”source ↗
“We started using AI at the hospital I work for and it’s still making mistakes summarizing basic data from the doctors’ notes and misses important information.”source ↗
“The AI transcription/summarization is mediocre at best compared to point solutions in that space. It's also in the small things, like why is there a creepy male voice that says "this meeting is being recorded" in every meeting out loud?”source ↗
“In the workflow automation, you have an AI editor that allows you to create your flow with AI. More often than not, it just says that it can do something and then proceeds to completely break the flow to then later reply: "you're right, this is not possible".”source ↗
“We're in discussions with our Hubspot AE to increase our seat count because of company expansion and they just added "$2,000 AI credits" to the quote, out of the blue.”source ↗
“I ask AI help in calculating some financial report ratio but they keep halucinating. Stupid AI cannot even properly scrape and calculate.”source ↗
“Even with strict roles within the agent and giving it plenty of resources and examples, it’s still creates results that are simply not true and reliable.”source ↗
“I honestly don’t know how to respond. It’s a client I’ve had for 15 years that is involved in the design industry. You would think they have respect for the process and not bring me and the designer into it after the tool has already been built with AI. I’m expected to support that whole process now?”source ↗
“We’ve had 2 clients express frustration with Hubspot’s AI - same with Notion, Riverside and other tools. All these legacy tools are trying to be Swiss Army wrappers of LLMs and it’s getting super messy.”source ↗
“In reality it only has about a 75% success rate which means we have to spend twice as long going over everything because we can't trust it.”source ↗
“we’ve spent infinitely more time hammering the models to spit out call notes, deal summaries, pipeline summaries, etc, than all of our users collectively have spent reading those things.”source ↗
“Please stop sending me 30 pages AI generated SOPs for me to "just review". I need to recreate them after reading them all and justifying why your draft makes no sense while pretending it is good but "just not quite there".”source ↗
“Charging for AI credits before the basics feel reliable would drive me mad, because at that point you’re paying extra to test their unfinished feature.”source ↗
“Some workflows genuinely save me hours; others just run out my tokens while making me just feel productive.”source ↗
“I had a habit of throwing problems at AI that never really needed it in the first place.”source ↗
“Oh my lanta, I went through this with ChatGPT for MONTHS!! I still do!! The amount of scam/fake/whatever claims I got in the past were wild because it either got reviews and feedback from another business, or it straight up assumed without confirming anything.”source ↗
“We hit the same wall with HubSpot's native AI and eventually stopped expecting it to do the heavy lifting inside the platform.”source ↗
“The problem for software factory is, that up to a certain point, you'll encounter some nasty bugs that are really important for your features, but you can't ship it because while the AI deals with the 90%, turns out that last remaining 10% are the one that make or break your project. And you need to deal with that remaining 10%. But how can you deal with the remaining 10% if you don't understand that 90% or majority of it?”source ↗
Where this came up
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