Have you ever found yourself in an endless back-and-forth with an AI assistant, trying to get exactly what you need? What should take minutes drags into a frustrating half-hour ordeal of clarifications, refinements, and starting over. Our team wondered: just how much time is poor prompting costing us? To find out, we conducted a series of time trials comparing basic prompts against carefully engineered ones for 10 everyday tasks. The results were eye-opening – and might forever change how you interact with AI tools.
PromptBetter AI offers a platform where users can refine and optimize their prompts across multiple AI models, helping to achieve the kind of time savings demonstrated in our experiments.
Our Testing Methodology
For this experiment, we recruited 20 participants with varying levels of AI experience, from complete beginners to regular users. Each participant was asked to complete 10 common tasks using three popular AI models (ChatGPT, Claude, and Gemini). For each task, participants were randomly assigned either:
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Basic prompts
: Simple, straightforward requests with minimal guidance
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Engineered prompts
: Carefully crafted inputs with specific parameters, context, and structure
We measured:
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Total time to acceptable completion (including all iterations)
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Number of follow-up prompts required
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User satisfaction with the final result (1-10 scale)
Let's dive into what we discovered.
Task 1: Writing a Professional Email
Basic prompt: "Write an email asking for a meeting with a client."
Engineered prompt: "Write a professional email to schedule a product demo with a potential client who expressed interest at last week's industry conference. The email should be warm but concise (150 words max), suggest two specific time slots next week, and briefly mention our product's main benefit."
Results:
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Basic prompt completion time: 8.5 minutes (average 3.2 follow-ups)
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Engineered prompt completion time: 2.3 minutes (average 0.4 follow-ups)
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Time saved
: 6.2 minutes (73% reduction)
The basic prompt produced generic emails that required multiple rounds of specification. Users spent time asking for tone adjustments, length changes, and adding missing details. The engineered prompt delivered ready-to-send emails that needed minimal tweaking.
Task 2: Creating a Content Outline
Basic prompt: "Make an outline about remote work."
Engineered prompt: "Create a structured outline for a 1,500-word blog post titled 'Remote Work in 2025: Trends, Tools, and Best Practices.' Include 4-5 main sections with 2-3 subsections each. The target audience is mid-level managers adapting to hybrid workforces. Include statistics or research points that should be incorporated."
Results:
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Basic prompt completion time: 12.7 minutes (average 4.1 follow-ups)
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Engineered prompt completion time: 3.2 minutes (average 0.8 follow-ups)
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Time saved
: 9.5 minutes (75% reduction)
The vague initial prompt created outlines that required extensive restructuring. Participants spent significant time specifying the content focus, depth, and organization. The engineered prompt produced comprehensive outlines that required only minor adjustments.
Task 3: Summarizing a Complex Document
Basic prompt: "Summarize this article." (followed by pasting a 2,000-word article about blockchain technology)
Engineered prompt: "Create a 250-word executive summary of the following article about blockchain applications in supply chain management. Focus on the key innovations, business benefits, and implementation challenges. Structure it with clear subheadings." (followed by the same article)
Results:
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Basic prompt completion time: 11.3 minutes (average 3.7 follow-ups)
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Engineered prompt completion time: 2.8 minutes (average 0.5 follow-ups)
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Time saved
: 8.5 minutes (75% reduction)
The basic approach produced summaries that were either too long, too short, or missed key points. Users spent time asking for revisions focused on specific aspects or requesting different formats. The engineered prompt delivered focused summaries that captured the essential information.
Task 4: Generating Data Analysis Code
Basic prompt: "Write code to analyze this data." (with a description of a dataset)
Engineered prompt: "Write Python code using pandas and matplotlib to analyze a CSV dataset of customer purchases. The code should: 1) Load 'customer_data.csv', 2) Calculate monthly sales trends, 3) Identify top 5 products by revenue, 4) Create a bar chart comparing sales by customer demographic, and 5) Export results to a new CSV. Include comments explaining each section."
Results:
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Basic prompt completion time: 18.2 minutes (average 5.3 follow-ups)
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Engineered prompt completion time: 4.1 minutes (average 1.1 follow-ups)
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Time saved
: 14.1 minutes (77% reduction)
The basic prompt created a frustrating experience where participants had to repeatedly specify what analysis they wanted, correct errors, and request additional features. The detailed prompt delivered functional code that needed only minor adjustments for specific requirements.
Task 5: Brainstorming Creative Ideas
Basic prompt: "Give me marketing ideas."
Engineered prompt: "Generate 7 creative social media campaign ideas for a sustainable fashion brand launching a summer collection. Each idea should include: 1) A catchy campaign name, 2) The primary platform (Instagram, TikTok, etc.), 3) A brief concept description (50 words max), and 4) One key performance metric to track success. Target audience is environmentally-conscious consumers aged 25-40."
Results:
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Basic prompt completion time: 14.5 minutes (average 4.8 follow-ups)
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Engineered prompt completion time: 3.5 minutes (average 0.7 follow-ups)
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Time saved
: 11 minutes (76% reduction)
The vague prompt resulted in generic marketing suggestions that participants had to repeatedly refine to get specific, actionable ideas. The engineered prompt delivered creative, targeted concepts that were immediately useful.
Task 6: Creating a Meeting Agenda
Basic prompt: "Make an agenda for my team meeting."
Engineered prompt: "Create a 60-minute agenda for a quarterly planning meeting with a 7-person product development team. The meeting needs to cover: Q1 results review (with space for metrics discussion), Q2 priorities, resource allocation, and team concerns. Format with time blocks, discussion leaders, and preparation required for each section."
Results:
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Basic prompt completion time: 9.7 minutes (average 3.4 follow-ups)
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Engineered prompt completion time: 2.1 minutes (average 0.3 follow-ups)
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Time saved
: 7.6 minutes (78% reduction)
The basic prompt created generic agendas that required multiple rounds of specification regarding meeting length, topics, and format. The engineered prompt delivered ready-to-use agendas that matched specific needs.
Task 7: Drafting a Product Description
Basic prompt: "Write a description for my new product."
Engineered prompt: "Write a compelling 200-word product description for an eco-friendly water bottle that keeps drinks cold for 24 hours or hot for 12 hours. Target audience is active professionals aged 25-45. Highlight its sustainable materials, unique design features, and practical benefits. Use a conversational tone with short paragraphs and incorporate 3 subtle calls to action throughout the text."
Results:
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Basic prompt completion time: 13.2 minutes (average 4.3 follow-ups)
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Engineered prompt completion time: 2.9 minutes (average 0.5 follow-ups)
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Time saved
: 10.3 minutes (78% reduction)
The basic prompt resulted in descriptions that lacked specificity, compelling features, or appropriate formatting. Participants spent time requesting tone changes, detail additions, and structural modifications. The engineered prompt produced market-ready descriptions.
Task 8: Creating a Learning Resource
Basic prompt: "Explain how to use Excel."
Engineered prompt: "Create a beginner-friendly tutorial explaining how to create and interpret pivot tables in Excel. Structure it as a step-by-step guide with 5-7 main steps, include a simple example scenario using sales data, explain 3 common mistakes to avoid, and add a quick reference section summarizing keyboard shortcuts. Use visual descriptions where helpful."
Results:
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Basic prompt completion time: 15.7 minutes (average 5.1 follow-ups)
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Engineered prompt completion time: 3.8 minutes (average 0.9 follow-ups)
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Time saved
: 11.9 minutes (76% reduction)
The broad request produced overwhelming, unfocused explanations that required extensive refinement to become useful teaching tools. The engineered prompt created focused, practical resources that needed minimal editing.
Task 9: Troubleshooting Technical Issues
Basic prompt: "Help me fix my WiFi problem."
Engineered prompt: "I need a systematic troubleshooting guide for resolving a WiFi connection issue on a Windows 11 laptop that shows 'Connected' but can't access websites. Provide a step-by-step diagnostic process starting with the simplest solutions first. For each step, explain: 1) What to check, 2) How to check it, 3) What the results indicate, and 4) What to try next based on findings."
Results:
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Basic prompt completion time: 16.3 minutes (average 5.5 follow-ups)
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Engineered prompt completion time: 3.7 minutes (average 0.8 follow-ups)
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Time saved
: 12.6 minutes (77% reduction)
The vague request led to generic suggestions that weren't applicable to specific situations. Users spent time providing additional details and context through multiple exchanges. The detailed prompt delivered targeted, actionable advice.
Task 10: Planning a Project Timeline
Basic prompt: "Help me plan my project."
Engineered prompt: "Create a 6-week project timeline for developing a mobile app MVP. Include the following phases: research, design, development, testing, and launch. Break each phase into 3-4 specific tasks with estimated durations, dependencies, and responsible team roles (designer, developer, QA, etc.). Format as a clear week-by-week schedule with milestones highlighted."
Results:
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Basic prompt completion time: 17.8 minutes (average 5.7 follow-ups)
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Engineered prompt completion time: 4.2 minutes (average 1.0 follow-ups)
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Time saved
: 13.6 minutes (76% reduction)
The simple request produced vague project outlines that required extensive follow-up to transform into actionable plans. The engineered prompt delivered comprehensive timelines that needed only minor customization.
Key Patterns and Insights
Across all 10 tasks, we observed consistent patterns:
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Average time reduction of 76%
: Well-engineered prompts reduced completion time by three-quarters on average.
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Dramatically fewer follow-ups
: Basic prompts required an average of 4.3 follow-up interactions, compared to just 0.7 for engineered prompts.
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Higher satisfaction scores
: Users rated results from engineered prompts 8.7/10 on average, versus 6.2/10 for basic prompts.
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Consistency across AI models
: While different models had varying strengths, the time savings from better prompting were consistent across all three AI assistants tested.
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Diminishing returns with experience
: Interestingly, experienced AI users saw smaller time savings (65%) compared to beginners (85%), suggesting prompt engineering skills become more intuitive with practice.
Practical Takeaways for More Efficient AI Interactions
Based on our findings, here are key elements that made prompts more time-efficient:
1. Specify Format and Structure
Telling the AI exactly how to organize information eliminates restructuring requests. Always specify length, format (bullets, paragraphs, tables), and organization.
2. Provide Complete Context
Include audience, purpose, tone, and relevant background. Every piece of context you provide upfront saves a follow-up round later.
3. Define Parameters and Constraints
Set clear boundaries around what you want: word count, number of items, complexity level, and any specific inclusions or exclusions.
4. Break Complex Tasks into Components
For multi-part outputs, clearly enumerate the elements you need rather than hoping the AI will include everything.
5. Use Precision Instead of Generalities
Replace vague terms ("good," "professional," "detailed") with specific descriptions of what these qualities mean in your context.
Conclusion
Our time trials demonstrate that prompt engineering isn't just about getting better quality outputs—it's about getting them significantly faster. The average 76% reduction in completion time represents enormous potential efficiency gains for individuals and organizations that regularly use AI tools.
The most striking insight is that investing just a few extra seconds in crafting a comprehensive prompt can save many minutes in back-and-forth refinement. This "prompt ROI" becomes even more significant for complex tasks or when working with multiple AI requests daily.
For those looking to maximize their productivity with AI assistants, developing strong prompt engineering skills clearly pays dividends in saved time and reduced frustration. Whether you're writing content, analyzing data, or planning projects, the quality of your prompt directly impacts the efficiency of your workflow.
Ready to see how much time better prompts could save in your own work? Start by upgrading one of your common AI requests using the principles outlined above, and experience the difference for yourself.
