According to Inc, AI business leader Allie K. Miller identified the number one mistake people make when using AI systems: failing to provide enough context. During an appearance on the Mel Robbins Podcast, Miller explained that vague prompts like “Plan me a family vacation to Greece” don’t contain enough detail for accurate responses. She emphasized that strategically curating your request with specific details about your family, past vacations, and preferences makes all the difference in getting relevant answers. The early-rate deadline for the 2026 Inc. Regionals Awards is Friday, November 14, at 11:59 p.m. PT, with applications now open. Miller’s insights come as businesses increasingly rely on AI tools that require proper prompting techniques to deliver value.
Why context actually matters
Here’s the thing about AI – it’s incredibly powerful but fundamentally stupid. These systems don’t have common sense or background knowledge about your specific situation. When you ask “Plan me a family vacation,” the AI doesn’t know if your family includes toddlers who need nap schedules or teenagers who want nightlife. It doesn’t know if you’re on a tight budget or looking for luxury. Basically, you’re asking a blindfolded person to hit a target you haven’t even described.
Miller’s apartment organization example really drives this home. Asking AI to “help organize my apartment” is like telling a personal assistant “make my life better” – it’s so broad that any response will be generic and probably useless. But when you provide photos, measurements, and specific concerns? Now you’re giving the AI something concrete to work with. The difference in output quality is night and day.
The time tradeoff that isn’t
So why do people keep making this mistake? I think it’s because we’re trained by search engines. We type “best pizza near me” into Google and get decent results. But AI doesn’t work that way – it needs the context that we normally provide through multiple searches and filters. Providing details upfront feels like extra work, but Miller’s absolutely right that it saves time overall.
Think about it: if you spend five minutes crafting a detailed prompt and get a usable answer immediately, versus spending thirty seconds on a vague prompt and then twenty minutes refining through follow-up questions… which approach actually saves time? The math isn’t complicated. And let’s be honest – how many times have you given up on an AI conversation because it just wasn’t getting what you wanted?
This goes beyond ChatGPT
Now here’s where this gets really interesting for business applications. The same principle applies whether you’re using consumer chatbots or enterprise AI tools. When you’re working with industrial AI systems that control manufacturing processes or analyze production data, vague prompts can lead to costly mistakes. The stakes are much higher than planning a vacation.
For companies implementing AI in industrial settings, proper prompting becomes critical. You can’t just tell a quality control AI “find defects” – you need to specify what types of defects, under what lighting conditions, with what tolerance levels. This is where expertise in crafting effective prompts separates successful AI implementations from expensive failures. The full interview with Miller on the Mel Robbins Podcast delves deeper into these business applications.
Changing how we approach AI
The real takeaway here is that we need to stop treating AI like magic and start treating it like a very capable but context-blind assistant. The tools are incredible, but they’re only as good as the instructions we give them. Miller’s advice isn’t just about getting better vacation plans – it’s about fundamentally changing how we interact with technology that’s becoming embedded in everything we do.
So next time you’re frustrated with an AI’s response, ask yourself: did I give it enough to work with? Because the problem might not be the AI’s intelligence – it might be the quality of your question. And honestly, isn’t that usually the case with human conversations too?
