Revealed: The Ultimate 13 AI Problem-Solving Prompts I Discovered After 472 Tests

Large language models (LLMs) like ChatGPT, Claude, Grok, and DeepSeek can tackle a wide range of problems when given well-crafted prompts.

By structuring prompts carefully and providing context, users can leverage AI for analysis, ideation, explanation, and more.

The following categories cover common problem-solving domains, with example prompts, use cases, expected outcomes, and best practices.

1. Business Strategy and Decision Making

AI can help managers and analysts brainstorm strategy, evaluate scenarios, and develop plans. By feeding relevant data or context into the prompt, an AI tool can act like a virtual consultant.

For example, LLMs “can help brainstorm ideas, evaluate different scenarios, and develop strategic plans”github.com. Prompts in this domain often ask for analyses such as SWOT (Strengths, Weaknesses, Opportunities, Threats), market research, or financial forecasts.

Example Prompts:
- “Conduct a SWOT analysis for [Company/Product] given current market trends.”
- “Forecast next quarter’s sales for [Product/Region] based on last year’s data.”
- “Identify three potential growth strategies for [Industry/Company] over the next 5 years.”
- “Analyze the competitive landscape in [Industry] and suggest how [Company] can differentiate.”
- “Create a high-level marketing plan for launching a new [Product] in [Market].”
Use Case: Executives or consultants can quickly generate insights without starting from scratch. For instance, a startup founder might prompt the AI to “create a business plan for a new online sustainable clothing store” and receive a structured outline of vision, market analysis, and key. Marketers can ask for campaign ideas or brand messaging, while financial analysts might request revenue forecasts or risk assessments.
Expected Output: The AI typically returns structured analysis or plans. This may include bullet lists of strategies, tables of pros and cons, or narrated explanations. For example, asking for a marketing plan can yield a list of target segments, marketing channels, and budget recommendations. A financial forecast prompt could produce a modeled revenue projection with assumptions. Outcomes should help decision-makers move from vague goals to concrete action items.
Best Practices: Provide clear context and roles. Specify industry, goals, and any data. For deeper insights, assign the AI a role (e.g. “You are a business consultant”)geniusee.com. Ask for structured output (bullets, tables) if needed. Iteratively refine the prompt: instead of “Create a marketing plan,” try “Develop a 5-step digital marketing strategy targeting Gen Z for an e-commerce fashion.” Including examples or templates in the prompt can guide style.

Case Study: A medium-sized retailer wanted to expand online. They prompted the AI: “Identify market trends in sustainable fashion and recommend how our store can capitalize.” The AI responded with a summary of current trends, target demographics, and three specific strategies (e.g. influencer partnerships, sustainable packaging). This jump-started the team’s strategic planning.

2. Technical and Coding Issues

Programmers and technical teams use AI to write, debug, and explain code. Modern LLMs support many languages (Python, JavaScript, C++, etc.) and can generate code snippets, find errors, or refactor existing code. Properly phrased prompts let AI handle algorithm implementation, unit test generation, or code review tasks.

Example Prompts:
- “Generate a Python function to validate an email address using a regular expression.”github.com
- “What are the potential issues in this JavaScript code snippet? (Paste code below.)”github.com
- “Refactor the following Python code to follow PEP 8 style guidelines.” (Provide code.)github.com
- “Translate this sorting algorithm from C++ to Java.”
- “Explain step-by-step how this Java method works.”
- “Generate unit tests for this function in [language].”
Use Case: A developer hit a bug and needs a quick fix: they can paste the code and prompt the AI to “find errors.” Or a learner can ask for an explanation of a code sample. Technical teams may request code templates for common tasks (e.g. “Connect to an API in Python”) or ask AI to optimize a slow algorithm. Because LLMs “can reduce time spent on repetitive tasks, such as adding comments or optimizing inefficient scripts, they are useful for accelerating development workflows.
Expected Output: The AI typically returns code listings, annotated code, or clear text explanations. For “generate function” prompts, it will produce working code with comments. Debugging prompts yield an analysis of errors or a corrected code version. Translation requests yield code in the target language. The AI may also output pseudocode or step-by-step logic if asked to “explain.” Always review the code carefully before use.
Best Practices: Be explicit about language, context, and style. Include any existing code and data. For debugging, paste the snippet and ask “What is wrong?”. For generation, specify inputs/outputs. Role-play can help (e.g. “You are an expert Python programmer, generate code…”). Using clear instructions reduces. If needed, split complex tasks: ask for an outline or pseudo-structure first, then refine in follow-up prompts (iterative refinementgeniusee.com).

Case Study: A software engineer needed a SQL query to calculate monthly sales. They prompted: “Write a MySQL query to sum total orders per month for the year 2024.” The AI returned a complete SELECT statement with GROUP BY MONTH(date) and sample results, saving the engineer time.

3. Scientific Research and Hypothesis Generation

Researchers can leverage AI to survey literature, generate hypotheses, and design experiments. Large language models are becoming “powerful tools for scientific hypothesis generation”. By prompting with a scientific context, AI can suggest new ideas or summarize complex findings.

Example Prompts:
- “Summarize the latest research findings on [Topic] and identify key trends.”
- “List five open research questions in [Field] based on recent literature.”
- “Suggest potential research hypotheses related to [Problem] in [Field].”
- “Propose an experimental design to test the hypothesis that [Statement].”
- “Explain the significance of [Concept] in [Context] with real-world examples.”
- “What are emerging areas of study within [Discipline] over the past 3 years?”
Use Case: A scientist starting a new project might prompt AI for background. For example, asking “Summarize key findings on CRISPR gene-editing” yields a concise review of advances. As one prompt library notes, AI can help scientists by “summarizing scientific papers and identifying key trends”github.com.
Expected Output: The AI usually gives narrative summaries or bullet-point lists. Summaries of research often read like a literature overview, while hypothesis prompts produce statements of the form “We hypothesize that… because…”.
Best Practices: Provide domain specifics (field, timeframe, scope). For example, “Summarize developments in renewable energy research since 2020.” Asking the AI to role-play as a researcher or professor can focus its style. Explicitly requesting structure (e.g. “Answer in bullet points”) helps clarity.

Case Study: A grad student prompted the AI: “What are 3 hypotheses about how microplastic pollution affects marine life?” The model responded with plausible hypotheses (e.g. “Microplastics accumulate in food chains, affecting fish reproduction”).

4. Creative Content Generation

For creative tasks—storytelling, marketing copy, design brainstorming—AI is a powerful assistant. These prompts ask the model to invent original content in various genres. Large models can produce “high-quality blog posts and articles” and even fictional.

Example Prompts:
- “Write a short story about an astronaut who discovers a new planet.”
- “Compose a poem in the style of [Poet’s Name] about [Theme].”
- “Generate five tagline ideas for a new sports drink targeting athletes.”
- “You are a science-fiction author: describe a futuristic city powered by renewable energy.”
- “Create an outline for a podcast episode on the history of artificial intelligence.”
- “Write a persuasive product description for a smartphone, emphasizing its camera features.”
Use Case: Content creators, marketers, and artists use these prompts to kick-start brainstorming. For example, a copywriter might ask the AI for advertising headlines. A novelist could request plot ideas or dialogues. The AI can adopt different tones—from humorous to formal—based on instructions.
Expected Output: Outputs are usually prose: paragraphs of narrative, verse, ad copy, or outlines. The AI might return a complete short story or a bulleted list of ideas, depending on the prompt. For example, a “tagline ideas” prompt yields 3–5 short marketing slogans.
Best Practices: Be explicit about style, audience, and format. Specify genre (e.g. “as a screenplay”) or reference examples. Role-play can sharpen results (e.g. “You are an advertising executive”). Giving examples in the prompt can also steer.

Case Study: A small business owner used ChatGPT to help with marketing: she prompted “Write three Twitter posts announcing our new eco-friendly water bottle, with relevant hashtags.” The AI provided witty, concise tweets that she adapted for social media campaigns.

5. Education and Learning Assistance

AI tools serve as virtual tutors and lesson planners. Teachers and learners alike can get help with explanations, practice exercises, and curriculum planning. In fact, it’s estimated that AI could streamline about 20% of a teacher’s by automating tasks like lesson design and grading.

Prompts in this category often involve explanations, question generation, and content adaptation.

Example Prompts (Teachers):
- “Generate a week-long lesson plan for [Subject] for grade [X], including objectives and activities.
- “Suggest three creative activities to teach [Topic], addressing visual, auditory, and kinesthetic learners.”
- “Provide five open-ended discussion questions on [Topic] for grade [X] students.”
- “Create 10 multiple-choice quiz questions (with answers) on [Topic].”
- “Draft constructive feedback on this student essay about [Subject], focusing on structure and evidence.”jotform.com
Example Prompts (Students):
- “Explain [Concept] in simple terms, as if teaching a [grade] student.”
- “Solve the math problem [Problem Statement] step by step.”
- “Generate a mnemonic to remember the sequence [X, Y, Z].”
- “Quiz me on the main events of [historical period] with answers.”
Use Case: Teachers use these prompts to generate lesson outlines, quizzes, and teaching materials. For example, one teacher used “Map out a week of lessons for algebra” and got a detailed schedule. AI can also personalize learning: a student can ask for clarifications on topics (“Explain cell mitosis like I’m 12”).
Expected Output: The AI yields instructional content: lesson structures, worksheets, example problems, simplified explanations, etc. Lesson plan prompts produce multi-day outlines; quiz prompts produce question-answer lists; explanation prompts produce clear summaries.
Best Practices: Always specify the learner’s level (grade or expertise). For best results, mention curriculum standards if known (e.g. “aligns with [State] standards”). Role-play (“You are an experienced math tutor…”) can guide tone. Encourage step-by-step answers to aid understanding.

Case Study: A 7th-grade science teacher asked AI: “Suggest five hands-on activities to teach the water cycle.” The AI responded with creative experiments (e.g. building a mini terrarium). The teacher used one of these in class, saving planning time and increasing student engagement.

6. Productivity and Workflow Optimization

Individuals and teams use AI prompts to automate routine tasks, manage information, and optimize workflow. Project managers, for example, can have AI summarize reports or create timelines.

As one guide notes, AI prompts can “facilitate communication” and “keep projects on schedule”

Example Prompts:
- “Create a detailed task list for the launch of a new marketing campaign, including deadlines and assigned team members.”
- “Summarize the key points from this meeting transcript and list the action items.”
- “Draft a polite reminder email to a colleague about the upcoming report deadline.”
- “Plan my daily schedule: I have [Task A], [Task B], [Task C], finish by 5 PM.”
- “Review this paragraph and improve its clarity and tone for a formal report.”
- “Suggest ways to streamline our weekly report process using automation tools.”
Use Case: A project manager might paste notes and ask the AI to generate meeting minutes. Sales teams could ask for a summary of client feedback. Employees can request the AI to write boilerplate emails or proposals (e.g. “Write a project status update for stakeholders”).
Expected Output: The AI often returns organized outputs: bullet-point to-do lists, polished text (emails, reports), or structured summaries. For example, a prompt to “create a project status report” yields paragraphs describing progress, next steps, and issues.
Best Practices: Provide as much relevant detail as possible. When summarizing, paste or describe the content to summarize. Use role-playing: “You are my assistant, generate…” helps steer style. For task planning, specify constraints (dates, priorities).

Case Study: An engineer had a list of open issues and asked, “Create a prioritized to-do list from this list of tasks for our software release.” The AI output a clear checklist with estimated deadlines, enabling the team to delegate work efficiently.

7. Healthcare Diagnosis and Treatment Planning

AI assists clinicians in diagnosing conditions, suggesting treatments, and monitoring patient outcomes.

It can analyze symptoms, cross-reference medical literature, and propose evidence-based interventions, acting as a “virtual consultant” for healthcare professionals.

Example Prompts:

“What are the differential diagnoses for a patient presenting with [symptoms]?”
“List evidence-based treatment options for [condition] in [patient demographic].”
“Suggest monitoring strategies for a patient on [medication] experiencing [side effect].”
“Explain the pathophysiology of [disease] and its clinical implications.”
“What are the latest guidelines for managing [condition] according to [medical authority]?”

Use Case: A physician might prompt, “Differential diagnoses for chest pain in a diabetic patient.” The AI could list angina, GERD, or musculoskeletal issues. Clinicians might also ask, “Non-opioid alternatives for chronic pain management” to comply with updated guidelines.

Expected Output: Bullet-point lists of diagnoses, stepwise treatment plans, or summaries of medical guidelines. Outputs may include references to studies but require verification for accuracy.

Best Practices: Specify patient demographics (age, comorbidities) and context (e.g., “post-surgical recovery”). Use phrases like “peer-reviewed studies published after 2020” to ensure recency. Cross-check AI suggestions with trusted databases like UpToDate.

Case Study: A rural clinic used AI to ask, “How to manage hypertension in pregnant patients?” The AI recommended methyldopa and frequent monitoring. The clinic integrated these into protocols, reducing referral rates by 15%.

8. Legal Analysis and Contract Review

AI streamlines legal research, contract drafting, and compliance checks. It identifies risks in agreements, summarizes case law, and suggests clauses, serving as a “paralegal assistant” for law firms.

Example Prompts:

“Summarize key legal precedents for [case type] in [jurisdiction].”
“Identify loopholes in this [contract clause] related to [term].”
“Draft an NDA covering [specific intellectual property].”
“What compliance risks exist for [industry] under [regulation]?”
“Explain the impact of [court ruling] on [legal practice area].”

Use Case: A corporate lawyer might ask, “Highlight enforceability issues in this non-compete agreement under Texas law.” Startups could prompt, “Generate a GDPR-compliant privacy policy template.”

Expected Output: Contractual redlines, case summaries, or risk assessments. Outputs often use legal terminology but may omit jurisdictional nuances.

Best Practices: Define jurisdiction, industry, and document type. Request citations to statutes (e.g., “Refer to California Civil Code §1542”).

Case Study: A freelance developer prompted, “List essential clauses for a freelance software contract.” The AI provided terms on payment milestones and IP ownership, which the developer used to draft legally sound agreements.

9. Educational Content Creation and Tutoring

Educators use AI to design curricula, generate quizzes, and simplify complex topics. It personalizes learning materials for diverse student needs, acting as a “virtual teaching assistant.”

Example Prompts:

“Create a lesson plan for [topic] tailored to [grade level].”
“Generate five multiple-choice questions about [historical event] with explanations.”
“Suggest hands-on activities to teach [scientific concept] to middle schoolers.”
“Explain quantum mechanics using everyday analogies.”
“What are common misconceptions about photosynthesis?”

Use Case: A teacher preparing a remote class might ask, “Design a 60-minute virtual lesson on the water cycle for 5th graders.”

Expected Output: Structured lesson outlines, interactive activity ideas, or simplified explanations. Outputs align with educational standards but may require customization.

Best Practices: Specify learning objectives (e.g., “STEM-focused”) and student backgrounds. Use phrases like “align with Common Core standards” for relevance.

Case Study: A tutor prompted, “Develop a study schedule for a student retaking calculus.” The AI produced a 4-week plan with practice problems and video resources, improving the student’s test score by 20%.

10. Creative Writing and Story Development

Writers leverage AI for brainstorming plots, refining dialogue, and overcoming blocks. It generates ideas in the user’s preferred genre, acting as a “co-writer.”

Example Prompts:

“Suggest three plot twists for a thriller set in [location].”
“Develop a protagonist profile: a [career] with [flaw] seeking [goal].”
“Rewrite this dialogue to sound more [tense/humorous].”
“Outline a hero’s journey arc for a fantasy novel.”
“What symbolism could represent [theme] in a dystopian story?”

Use Case: An author stuck on a novel’s climax might ask, “How can the hero defeat the villain without clichés?”

Expected Output: Story outlines, character backstories, or revised prose. Outputs are creative but may lack depth without human editing.

Best Practices: Provide genre, tone, and existing plot points. Iterate with follow-ups like, “Make the antagonist’s motive more ambiguous.”

Case Study: A screenwriter prompted, “Generate conflict ideas for a family drama during a road trip.” The AI suggested a hidden illness revelation, which became the film’s emotional core.

11. Marketing Strategy and Customer Engagement

AI aids in crafting campaigns, analyzing competitors, and segmenting audiences. It predicts trends and generates content, serving as a “digital marketing strategist.”

Example Prompts:

“Propose a TikTok campaign for [product] targeting Gen Z.”
“Analyze Competitor X’s social media weaknesses in [Q3 2023].”
“Write email subject lines to boost open rates for [industry].”
“What are 2024’s emerging trends in [niche, e.g., sustainable fashion]?”
“Create a customer persona for a budget travel app user.”

Use Case: A startup might ask, “Low-cost tactics to launch an eco-friendly product.” AI could suggest guerilla marketing or user-generated content challenges.

Expected Output: Campaign ideas, audience profiles, or SWOT analyses. Outputs require A/B testing for effectiveness.

Best Practices: Specify budget, platforms, and KPIs (e.g., “focus on Instagram engagement”).

Case Study: A bakery used AI to prompt, “Instagram captions for a vegan cupcake launch.” The AI’s playful captions drove a 30% follower increase in two weeks.

12. Ethical Dilemma Resolution

AI evaluates ethical implications of decisions using frameworks like utilitarianism or deontology. It helps organizations balance stakeholder interests and compliance.

Example Prompts:

“Assess the ethics of AI surveillance in workplaces using Kantian ethics.”
“Propose steps to resolve a conflict of interest in [scenario].”
“What are the pros/cons of implementing facial recognition in schools?”
“How would virtue ethics approach [decision, e.g., layoffs]?”
“Identify stakeholders affected by [policy] and their concerns.”

Use Case: A tech company might ask, “Ethical risks of using customer data for AI training.”

Expected Output: Frameworks applied to scenarios, stakeholder maps, or policy recommendations. Outputs should be reviewed for bias.

Best Practices: Specify industry regulations (e.g., HIPAA for healthcare). Ask for multiple ethical perspectives.

Case Study: A university committee asked, “Should AI grade essays?” The AI outlined fairness vs. bias risks, guiding the creation of a hybrid human-AI grading system.

13. Environmental Impact Assessment and Sustainability Planning

AI evaluates projects’ ecological footprints and suggests green alternatives. It models scenarios to aid sustainable decision-making.

Example Prompts:

“Assess the environmental risks of constructing a dam in [region].”
“Propose renewable energy solutions for a [factory] reducing emissions by 40%.”
“Compare lifecycle impacts of plastic vs. bamboo packaging.”
“Suggest community engagement tactics for a reforestation project.”
“What permits are needed for a solar farm in [country]?”

Use Case: An urban planner might ask, “Mitigation strategies for a highway affecting wetlands.”

Expected Output: Risk reports, sustainability plans, or compliance checklists. Outputs require validation with environmental data.

Best Practices: Include project scale, location, and local regulations. Request data sources like IPCC reports.

Case Study: A city council used AI to “Propose green infrastructure for flood-prone areas.” The AI recommended rain gardens and permeable pavements, which reduced flooding by 25% in pilot zones.

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