What to ask the AI tool you’re evaluating to avoid wasting time and money
In the rush to integrate AI, many companies are making a dangerous mistake: plugging in ChatGPT and calling it their “AI product.” It’s an easy trap to fall into - ChatGPT is really really smart. And we have no problem with “GPT wrappers.” We utilize models by OpenAI where they fit, and think ChatGPT has amazing capabilities for generalized problems.
But just because an AI can generate content doesn’t mean it’s the right AI for the job.
If you’re evaluating an AI tool for your business, whether for auto parts, e-commerce, or enterprise automation, there are key questions you should ask before you commit. Otherwise, you risk wasting time, money, and resources on a solution that won’t work.
The Danger of AI Shortcuts
Too many companies treat AI as a plug-and-play solution. They drop a generic model like ChatGPT into their system, slap their brand name on it, and hope it delivers results. For industry specific applications, this is a dangerous mistake:
- Generic AI is not domain-specific – It lacks knowledge of industry-specific data structures, product categories, and technical terminology.
- It can’t handle structured data well – ChatGPT is great for writing essays but struggles with structured data formats and character limits.
- It hallucinates – If you ask it for a specific auto part specific information, it might generate information that simply doesn’t exist.
- It lacks control – AI that isn’t fine-tuned can’t reliably follow business logic, brand guidelines, or compliance requirements.
Key Questions to Ask Your AI Provider
For a long time, AI has been seen as a “black box” - something really cool but really unpredictable. The goal of any AI startup is to demystify this process as much as possible within its product, and for its customers.
To avoid wasting your time and money, don’t just accept an AI tool at face value. Ask these critical questions before investing in an AI-powered product:
1. How did you fine-tune for our industry?
- AI models need custom fine-tuning to work well in niche industries. If the provider hasn’t trained the model on your industry’s specific data, it won’t produce accurate results.
- Ask: “What data sources were used to fine-tune this AI? How often is the model retrained? Can it handle structured and unstructured data?”
2. Which AI models were picked and why?
- Not all AI models are created equal. Some are better at language, others at classification, and others at data processing. If the company just uses ChatGPT without modifications, that’s a red flag.
- Ask: “Did you build a proprietary model? Are you using GPT-4, Claude, or a mix of different models? How was the model selection process determined?”
3. How do you prevent hallucinations?
- Hallucinations are when AI generates incorrect, misleading, or completely false information. There are different types:
- Factual hallucinations – AI generates fake facts
- Context hallucinations – It applies information from one case to another incorrectly (e.g., assuming an air filter for a 2018 Ford F-150 fits a 2019 model without verification).
- Structural hallucinations – AI misses character limits and formatting rules
- Ask: “What safeguards do you have in place to detect and eliminate hallucinations? Are there human validation checks?”
4. How do you ensure data accuracy and validation?
- AI is only as good as the data it’s trained on. If the provider can’t explain how they validate the AI’s accuracy, they may just be repackaging generic AI.
- Ask: “How do you verify AI-generated data? What systems are in place to check for inconsistencies or errors?”
5. Can you handle edge cases?
- AI needs to be able to handle rare or unusual inputs without breaking. In auto parts, that might mean recognizing discontinued products, regional differences, or obscure part types.
- Ask: “How does your AI perform when dealing with edge cases? Do you have real-world examples?”
AI isn’t magic - it’s a tool. And like any tool, it needs to be built, tested, and fine-tuned for the job at hand. If a company can’t answer these questions, they’re likely selling a generic, unreliable AI solution that will cost you in the long run.
Instead of falling for AI shortcuts, invest in solutions designed for your industry. Whether it’s auto parts, retail, or enterprise software, choosing the right AI before you start will save you from expensive mistakes later. The following blog posts will share how Versable addresses each of these questions to be the market leading AI tool for the aftermarket. Stay tuned!
PS: welcome to our first blog post! I'm Christina - founder and CEO of Versable. We're always looking for guest collaborators on future blog posts. If you want to discuss data, AI, or the aftermarket and are interested, shoot me an email at tina@versable.ai
