Created by Hisham Basheer (HSBR)
Compare AI image generation results using basic vs. detailed prompts
Use any AI image generation tool (ChatGPT with DALL·E, Gemini, Freepik AI, Ideogram AI, Leonardo AI, etc.) to generate an image twice — first with a basic prompt, then with a detailed one. Compare the difference and understand how prompt engineering dramatically affects AI-generated outputs.
The simple prompt produced a generic interpretation with basic elements, while the detailed prompt generated a highly specific scene with all requested elements precisely positioned and styled.
Simple prompt: AI made assumptions about lighting, setting, and environment. Detailed prompt: Every environmental aspect was controlled - late-night atmosphere, home office setting, specific lighting conditions.
The detailed prompt's inclusion of aspect ratio (16:9), camera angle (wide shot), and style (realistic, cinematic) resulted in a more professional, purposeful composition.
Simple prompt: Generic computer setup. Detailed prompt: Specific Mac setup with three monitors, wireless peripherals, sticky notes, visible to-do list, tea cup - all elements present as requested.
The detailed prompt produced a more cinematic, professional-grade image with intentional mood and atmosphere, while the simple prompt resulted in a more straightforward, literal interpretation.
The detailed prompt image is significantly better and matched my mental visualization perfectly. It captured exactly what I envisioned - a professional developer's late-night workspace with the Mac setup, three monitors, dim lighting with screen glow, sticky notes, to-do list, and tea cup. The simple prompt was too vague and gave AI too much creative freedom, resulting in a generic scene that didn't capture the specific atmosphere I wanted.
The simple prompt result produced several unexpected choices - random lighting that didn't match the late-night mood, generic desk setup without the specific Mac three-monitor configuration, missing key elements like sticky notes and to-do list, and overall lack of the professional, cinematic quality I wanted. The AI made its own assumptions based on training data rather than my specific vision. This is a clear example of why detailed prompts are crucial for professional work.
The detailed prompt is good because it dramatically elevated the artistic quality. By specifying realistic cinematic style, 16:9 ratio, late-night lighting with screen glow, wide shot framing, and professional infrastructure with LED backlighting, I got exactly the mood and composition I envisioned. The detailed prompt transformed a basic concept into a professionally composed scene with intentional atmosphere, proper lighting, and cinematic quality - something the simple prompt couldn't achieve.
Professional Projects: I would always use detailed prompts for professional work. When creating content for clients, portfolios, marketing materials, or documentation, I need precise control over every element - lighting, composition, style, and specific details. Detailed prompts ensure consistency, meet exact specifications, and prevent costly revisions. This is non-negotiable for professional deliverables.
Creative/Exploratory Projects: For brainstorming or exploring new ideas, I might start with simple prompts to see what AI suggests, but even then, I'd quickly move to moderately detailed prompts to guide the direction. Simple prompts are useful only for initial exploration, not for final outputs.
My Takeaway: Detailed prompts are good and essential for getting professional results. The extra time spent crafting comprehensive prompts with specifications (subject, style, lighting, framing, environment, technical details) always pays off with precise, high-quality outputs that match your vision exactly.
Prompt engineering is a critical skill in AI content generation. This task demonstrates that the difference between a simple and detailed prompt is the difference between getting "something" and getting exactly what you need. Through this comparison, I learned that detailed prompts that specify subject, style, lighting, framing, angle, environment, and technical specifications produce professional-grade results that match creative vision precisely.
What I learned: The simple prompt "web developer sitting in-front of his work table" gave the AI too much freedom to interpret, resulting in generic choices. The detailed prompt with comprehensive specifications (16:9 ratio, cinematic style, late-night lighting, three-monitor Mac setup, specific accessories) produced an image that matched my exact vision. Every detail I specified appeared in the final result.
Real-world impact: For professional projects requiring brand consistency and specific aesthetics, detailed prompts are non-negotiable. They ensure deliverables meet exact specifications and prevent costly revisions. In creative exploration, simple prompts can inspire new directions, but even then, moderate detail helps guide AI creativity productively.
Takeaway: Mastering prompt engineering is essential for anyone working with AI tools in professional or creative contexts. The investment in crafting detailed, comprehensive prompts pays off with precise, professional results that match your vision. This skill will become increasingly valuable as AI tools continue to evolve and integrate into professional workflows.