Why spending more doesn’t always mean getting better results from AI
The Quick Answer: Good questions beat expensive AI every time. Know your goal, provide context, specify format, give examples, and iterate. Question engineering is the skill that multiplies AI value while reducing costs – regardless of which model you use.
Question Engineering for AI: The Art of Getting Better Answers; Why spending more doesn’t always mean getting better results from AI
Blog by Peter Hanley bizbitspro.com
The Expensive Mistake Most People Make
You could pay hundreds of dollars in API costs, upgrade to premium AI services, or run the most advanced models available – but if you’re asking the wrong questions, you’re essentially buying a Ferrari and driving it in first gear. The truth is, question engineering has become one of the most valuable skills in the AI era, yet it’s surprisingly undervalued.
Most users treat AI like a magic 8-ball: shake it with any question and hope for wisdom. But Large Language Models (LLMs) are more like sophisticated instruments that respond dramatically differently depending on how you tune them with your input.
Know Where You Want to Go
Before crafting your question, ask yourself: “What would success look like?” This isn’t just philosophical advice – it’s practical engineering.
Vague goal: “Help me with marketing” Clear destination: “Create three email subject lines for a SaaS product launch that increase open rates for B2B decision-makers”
The difference? The second version gives the AI a clear target, context, and measurable outcome. You’ve essentially provided a blueprint rather than asking it to guess what you need.
The Simple Rules That Change Everything
Rule 1: Context is Currency
AI doesn’t know what you know unless you tell it. Front-load your questions with relevant background.
Weak: “How do I fix this code?” Strong: “I’m building a React component for user authentication. This login function is throwing a ‘Cannot read property’ error on line 23 when users enter invalid credentials. Here’s the code: [paste code]”
Rule 2: Be Specific About Format
AI can give you a paragraph, a bullet list, a table, code, or even a poem. If you don’t specify, you get whatever the model defaults to.
Generic: “Tell me about competitor analysis” Formatted: “Create a competitor analysis table comparing 5 key features across 3 main competitors in the project management software space”
Rule 3: Use Progressive Refinement
Don’t expect perfection on the first try. Treat your initial question as a rough draft, then iterate.
Round 1: “Write a product description for noise-canceling headphones” Round 2: “Make it more technical, focusing on the 30dB noise reduction and frequency response” Round 3: “Now rewrite it for audiophiles who care about soundstage and impedance”
Rule 4: Provide Examples
When you want something specific, show the AI what “good” looks like.
“Write three headlines for this article. Here are examples of the style I want:
- ‘The Hidden Cost of Remote Work Nobody Talks About’
- ‘Why Your Productivity System is Actually Making You Less Productive’
- ‘The One Meeting Rule That Saved Our Startup $50K'”
Rule 5: Set Constraints
Limitations often produce better results than unlimited freedom.
Unlimited: “Write about climate change” Constrained: “Write a 200-word explanation of carbon pricing that a small business owner could understand in 2 minutes”
The Meta-Question Technique
Here’s an advanced strategy: ask the AI to help you ask better questions.
“I want to improve my company’s customer retention. Before you give me advice, what additional information would help you provide more targeted recommendations?”
This approach often reveals gaps in your thinking and helps you provide more useful context upfront.
Common Question Engineering Mistakes
The Kitchen Sink Problem
Loading your question with every possible detail. AI handles complexity well, but focus beats comprehensiveness.
The Assumption Trap
Assuming the AI understands your industry jargon, company situation, or implicit requirements. Always define your terms and context.
The Single-Shot Syndrome
Expecting perfect results from one interaction. Professional question engineering is inherently iterative.
The Format Fumble
Not specifying how you want the information presented. A table might serve you better than paragraphs, or vice versa.
LLMs Have Made It Easier (And Harder)
Modern LLMs are remarkably forgiving. They can work with imperfect questions, fill in gaps, and even correct obvious mistakes in your prompt. This has made AI more accessible but also created a false sense that question quality doesn’t matter.
The reality? Better questions don’t just get you better answers – they get you better answers faster, with less back-and-forth, and often at lower cost if you’re paying per token.
The ROI of Good Questions
Consider two approaches:
Approach A: Ask 10 vague questions, get mediocre answers, spend time clarifying and re-asking Approach B: Spend 5 minutes crafting 1 well-engineered question, get a targeted answer immediately
Approach B wins on time, cost, and result quality. The math is simple: investing in question engineering pays compound returns.
How to find answers to questions asked
Practical Framework: The CLEAR Method
Context: What background does the AI need? Length: How long should the response be? Examples: Can you show what good looks like? Audience: Who is this for? Result: What specific outcome do you want?
Example using CLEAR: “Context: I’m launching a B2B SaaS tool for small marketing agencies. Length: I need 3 short paragraphs. Examples: Think Mailchimp’s friendly but professional tone. Audience: Marketing agency owners who are overwhelmed with client work. Result: A landing page hero section that clearly communicates our value proposition of saving them 10 hours per week.”
The Future of Question Engineering
As AI becomes more sophisticated, the skill of question engineering will only become more valuable. The people who learn to communicate effectively with AI systems will have a significant advantage in almost every field.
Think of it this way: we’ve moved from a world where you needed to know how to find information to a world where you need to know how to extract the right information from an incredibly knowledgeable but literal partner.
Start Simple, Get Better
You don’t need to master advanced prompting techniques overnight. Start with these basics:
- State your goal clearly
- Provide relevant context
- Specify the format you want
- Give examples when possible
- Iterate based on results
Remember: the best AI users aren’t necessarily the most technical – they’re the ones who’ve learned to ask better questions. And that’s a skill worth developing, regardless of how much you’re willing to spend on AI tools.
The conversation between human and artificial intelligence is just beginning. Those who learn to speak this new language fluently won’t just save money – they’ll unlock possibilities that others miss entirely.