The Complete LLM Prompt Library: 30+ Tested Examples for Every Use Case

Large Language Models (LLMs) have transformed how we work, but their effectiveness depends entirely on how we communicate with them. A well-crafted prompt can mean the difference between generic output and breakthrough insights. This comprehensive prompt library provides battle-tested LLM prompt examples across every major use case, complete with explanations of why they work and how to adapt them.

Understanding What Makes Prompts Work

Before diving into our prompt engineering examples, it’s worth understanding the core principles that make prompts effective:

Specificity beats vagueness. LLMs respond best to clear, specific instructions. Instead of “write about AI,” try “write a 500-word technical explanation of transformer architecture for software engineers.”

Context enables precision. Providing relevant background information helps the model understand your exact needs. Include your role, the audience, constraints, and desired outcomes.

Structure guides output. When you specify format requirements—bullet points, numbered lists, code blocks, or specific sections—you get more usable results.

Examples demonstrate intent. Showing the model what you want (few-shot prompting) often works better than just describing it.

These principles aren’t theoretical. Teams that understand prompt fundamentals report 38% improvement in success rates—exactly what participants discover when completing our LLM Adventure interactive training.

Comprehensive AI Prompts by Category

Coding & Development Prompts

1. Code Review with Context

Review this [LANGUAGE] code for security vulnerabilities, performance issues,
and maintainability concerns. Focus on production-readiness:

[CODE BLOCK]

Provide:
1. Critical issues that must be fixed before deployment
2. Performance optimization opportunities
3. Security considerations
4. Recommended refactoring with examples

Why this works: Specifies the programming language, provides clear evaluation criteria, and requests structured output with actionable priorities.

Variation: Add specific concerns like “Focus on SQL injection risks” or “Optimize for mobile environments with limited memory.”

2. Architecture Design Assistant

I'm designing a [TYPE] application that needs to [PRIMARY FUNCTION].
Requirements:
- Expected scale: [USER COUNT/DATA VOLUME]
- Key constraints: [BUDGET/TIMELINE/TECH STACK]
- Must integrate with: [EXISTING SYSTEMS]

Suggest 3 architecture approaches with trade-offs for each.

Why this works: Defines scope, constraints, and decision criteria upfront, enabling comparative analysis rather than a single generic solution.

Variation: Add “Focus on serverless solutions” or “Prioritize cost optimization over performance.”

3. Debug Assistant with Stack Trace

I'm getting this error in [LANGUAGE/FRAMEWORK]:

[ERROR MESSAGE]

Stack trace:
[STACK TRACE]

Relevant code context:
[CODE BLOCK]

What's causing this error and how do I fix it? Include explanation of
why this error occurs and how to prevent it in the future.

Why this works: Provides complete context—error message, stack trace, and relevant code—enabling precise diagnosis rather than generic debugging advice.

Variation: Add “I’ve already tried [ATTEMPTED SOLUTIONS]” to avoid redundant suggestions.

4. API Documentation Generator

Generate comprehensive API documentation for this [LANGUAGE] function:

[CODE BLOCK]

Include:
- Purpose and use cases
- Parameters with types and constraints
- Return values and possible error states
- Usage examples with realistic data
- Common pitfalls and best practices

Why this works: Transforms code into complete documentation by specifying exactly what aspects to document, ensuring consistency across your codebase.

Variation: Specify documentation format: “Use JSDoc format” or “Generate OpenAPI 3.0 specification.”

Writing & Content Creation Prompts

5. Technical Explanation Translator

Explain [TECHNICAL CONCEPT] for [TARGET AUDIENCE] without losing accuracy.

Current technical description: [TECHNICAL VERSION]

The audience has [KNOWLEDGE LEVEL] and needs to understand this concept
to [SPECIFIC PURPOSE]. Use [ANALOGY TYPE] where helpful.

Why this works: Balances accuracy and accessibility by defining the audience, their knowledge level, and why they need to understand the concept.

Variation: Specify constraints like “in under 200 words” or “suitable for a 60-second video script.”

6. Content Structure Planner

I need to write a [CONTENT TYPE] about [TOPIC] for [AUDIENCE].

Goals:
- Primary: [MAIN OBJECTIVE]
- Secondary: [SUPPORTING GOALS]
- Constraints: [WORD COUNT/FORMAT/TONE]

Create a detailed outline with:
1. Hook that addresses [AUDIENCE PAIN POINT]
2. Main sections with key points
3. Supporting evidence needed for each section
4. Logical flow that leads to [DESIRED CONCLUSION]

Why this works: Separates planning from writing, ensuring structural soundness before investing time in full content creation.

Variation: Add “Include SEO keyword placement strategy” or “Suggest compelling headlines for each section.”

7. Email Response Optimizer

Transform this email response to be [TONE] while maintaining [KEY REQUIREMENTS]:

Original email I received:
[EMAIL CONTENT]

My draft response:
[DRAFT]

Maintain these key points: [MUST-INCLUDE ITEMS]
Change the tone to be: [DESIRED TONE]
Keep under: [WORD COUNT]

Why this works: Preserves your core message while optimizing tone and brevity—essential for professional communication.

Variation: Specify tone precisely: “diplomatically decline without burning bridges” or “express urgency without appearing demanding.”

8. Long-Form Content Expander

Expand this outline into a detailed [CONTENT TYPE] targeting [AUDIENCE]:

[OUTLINE]

For each section:
- Develop key points with supporting evidence
- Include relevant examples from [INDUSTRY/CONTEXT]
- Maintain [TONE] throughout
- Target [WORD COUNT] total
- Optimize for keywords: [KEYWORDS]

Why this works: Provides structural framework while ensuring consistency in tone, depth, and SEO optimization throughout longer pieces.

Variation: Add “Include expert quotes (mark as [PLACEHOLDER])” or “Weave in storytelling elements.”

Data Analysis & Research Prompts

9. Dataset Interpreter

Analyze this dataset and identify:

[DATA OR DESCRIPTION OF DATA]

1. Key patterns and anomalies
2. Correlation insights worth investigating
3. Potential data quality issues
4. Recommended visualizations to highlight findings
5. Questions this data raises but cannot answer

Context: [BUSINESS CONTEXT/GOALS]

Why this works: Guides systematic analysis while acknowledging data limitations—critical for sound decision-making.

Variation: Focus analysis: “Prioritize time-series trends” or “Focus on outlier detection.”

10. Research Synthesis

I'm researching [TOPIC] to answer [SPECIFIC QUESTION].

Sources reviewed:
1. [SOURCE 1 + KEY POINTS]
2. [SOURCE 2 + KEY POINTS]
3. [SOURCE 3 + KEY POINTS]

Synthesize these sources to:
- Identify consensus viewpoints
- Highlight conflicting findings with context
- Reveal research gaps
- Provide an evidence-based answer to my question
- Suggest next research directions

Why this works: Transforms information gathering into actionable insights by requiring synthesis rather than summary.

Variation: Add “Focus on peer-reviewed sources only” or “Prioritize recent findings from past 2 years.”

11. Comparative Analysis Framework

Compare [OPTION A] vs [OPTION B] for [SPECIFIC USE CASE]:

Evaluation criteria (in priority order):
1. [CRITERION 1 + WEIGHT]
2. [CRITERION 2 + WEIGHT]
3. [CRITERION 3 + WEIGHT]

Context: [CONSTRAINTS/REQUIREMENTS]

Provide:
- Side-by-side comparison table
- Strengths and weaknesses for each
- Recommendation with reasoning
- Risk assessment for each option

Why this works: Establishes clear evaluation framework before analysis, ensuring objective comparison aligned with actual needs.

Variation: Add “Include total cost of ownership analysis” or “Consider vendor lock-in risks.”

Business & Strategy Prompts

12. Market Analysis Prompt

Analyze the market opportunity for [PRODUCT/SERVICE] in [MARKET/GEOGRAPHY]:

Current market context:
- Target customer: [DESCRIPTION]
- Key competitors: [LIST]
- Market size: [DATA IF AVAILABLE]
- Regulatory environment: [RELEVANT FACTORS]

Provide:
1. Market sizing and growth trajectory
2. Competitive landscape analysis
3. Barriers to entry
4. Go-to-market strategy recommendations
5. Key assumptions and risks

Why this works: Structures strategic thinking while requiring explicit documentation of assumptions—essential for sound business planning.

Variation: Focus on specific aspects: “Prioritize competitive moat analysis” or “Focus on pricing strategy.”

13. Meeting Prep Assistant

I have a meeting about [TOPIC] with [ATTENDEES + ROLES] on [DATE].

Meeting goal: [PRIMARY OBJECTIVE]

Background context:
- Previous discussions: [SUMMARY]
- Current status: [STATE]
- Challenges: [BLOCKERS/CONCERNS]

Prepare:
1. Agenda optimized for [TIME DURATION]
2. Key questions to drive toward goal
3. Potential objections and responses
4. Decision framework
5. Success criteria for this meeting

Why this works: Transforms meetings from time sinks into strategic sessions by forcing clarity on objectives and success criteria.

Variation: Add “Include icebreaker for first-time attendees” or “Prepare for potential conflict on [ISSUE].”

14. Product Feature Prioritization

Help prioritize these feature requests for [PRODUCT]:

Features under consideration:
1. [FEATURE 1 + CUSTOMER REQUESTS]
2. [FEATURE 2 + CUSTOMER REQUESTS]
3. [FEATURE 3 + CUSTOMER REQUESTS]

Evaluation factors:
- Strategic alignment: [PRODUCT VISION]
- Available resources: [TEAM SIZE/TIMELINE]
- Technical debt considerations: [CONTEXT]

Create a prioritization matrix with:
- Impact vs. effort assessment
- Strategic fit scoring
- Recommended sequencing with rationale
- Quick wins to maintain momentum

Why this works: Balances customer requests, strategic goals, and practical constraints for evidence-based prioritization.

Variation: Add “Consider competitive pressure from [COMPETITOR]” or “Factor in revenue impact.”

Problem Solving & Critical Thinking

15. Root Cause Analysis

Problem: [PROBLEM STATEMENT]

Observable symptoms:
- [SYMPTOM 1]
- [SYMPTOM 2]
- [SYMPTOM 3]

Context:
- When it started: [TIMELINE]
- What changed recently: [CHANGES]
- Scope/scale: [IMPACT]

Use the 5 Whys method to:
1. Identify potential root causes
2. Distinguish symptoms from causes
3. Suggest verification methods for each hypothesis
4. Recommend corrective actions
5. Propose preventive measures

Why this works: Applies systematic problem-solving methodology while requiring testable hypotheses—preventing jumping to solutions.

Variation: Specify methodology: “Use fishbone diagram approach” or “Apply systems thinking framework.”

16. Decision Matrix Creator

I need to make a decision about [DECISION].

Options:
1. [OPTION 1]
2. [OPTION 2]
3. [OPTION 3]

Decision criteria (weight if known):
- [CRITERION 1]
- [CRITERION 2]
- [CRITERION 3]

Constraints:
- Must decide by: [DEADLINE]
- Budget: [CONSTRAINT]
- Other: [FACTORS]

Create a weighted decision matrix and identify:
- Which option scores highest
- Sensitivity analysis (how robust is this choice?)
- What additional information would change the decision
- Recommendation with confidence level

Why this works: Forces explicit weighting of criteria and sensitivity analysis—revealing decision robustness and information gaps.

Variation: Add “Include worst-case scenario analysis” or “Consider reversibility of each option.”

Learning & Education

17. Concept Breakdown Tutor

I'm trying to understand [COMPLEX CONCEPT].

My current understanding: [WHAT YOU KNOW]
What's confusing me: [SPECIFIC CONFUSION]
My background: [RELEVANT KNOWLEDGE]

Please:
1. Break down this concept into prerequisite components
2. Explain each component with examples
3. Show how they connect to form the complete concept
4. Provide an analogy that matches my [FIELD/INTEREST]
5. Give me a practice problem to test understanding

Why this works: Tailors explanation to existing knowledge and identifies specific confusion points rather than generic overviews.

Variation: Add “Use Socratic method with questions” or “Provide visual/spatial explanations.”

18. Study Guide Generator

Create a study guide for [TOPIC/EXAM]:

Material to cover:
[CONTENT OUTLINE OR RESOURCES]

Study constraints:
- Time available: [DURATION]
- Current knowledge level: [ASSESSMENT]
- Learning style: [PREFERENCES]
- Exam format: [TYPE]

Include:
1. Prioritized topic list (what matters most)
2. Study schedule breakdown
3. Active recall practice questions
4. Common misconception clarifications
5. Self-assessment checkpoints

Why this works: Creates personalized study plans based on actual constraints and learning science principles.

Variation: Add “Focus on areas I scored lowest: [WEAK AREAS]” or “Optimize for time efficiency.”

Advanced Prompting Techniques

19. Chain-of-Thought Reasoning

Solve this problem by thinking through it step by step:

Problem: [COMPLEX PROBLEM]

Show your reasoning process:
1. What information is given?
2. What information is missing?
3. What assumptions must we make?
4. Break the problem into sub-problems
5. Solve each sub-problem
6. Combine solutions
7. Verify the answer makes sense

Then provide the final answer with confidence level.

Why this works: Activates step-by-step reasoning, dramatically improving performance on complex problems compared to direct answers.

Variation: For math: “Show all calculations” or for logic: “Identify potential logical fallacies.”

20. Role-Based Perspective

Evaluate [SITUATION/DECISION] from the perspective of a [EXPERT ROLE]
with [YEARS] experience in [FIELD].

Situation:
[CONTEXT]

As this expert, provide:
1. Initial assessment based on pattern recognition
2. What details you'd want to investigate
3. Likely risks this expertise reveals
4. Recommendations with reasoning
5. What less experienced people might miss

Use the thought patterns and terminology authentic to this role.

Why this works: Accesses domain-specific knowledge patterns and terminology by explicitly invoking expert perspectives.

Variation: Compare multiple perspectives: “Now analyze from [DIFFERENT ROLE] perspective and identify conflicts.”

Modifying Prompts for Your Needs

The most valuable skill in prompt engineering isn’t memorizing examples—it’s understanding how to adapt them. Here’s how to modify any prompt from this AI prompts library:

Adjust Specificity

Too broad: Results lack actionable detail Too narrow: Overly constrained, missing creative solutions Solution: Start specific, then relax constraints if needed

Layer Context Progressively

Start with: Core task description Add: Constraints and requirements Include: Success criteria Finish with: Output format specifications

Experiment with Temperature

For these LLM prompt examples:

• Creative tasks: Request “3 diverse approaches” or “think creatively”
• Analytical tasks: Specify “use data-driven reasoning” or “show calculations”
• Balanced tasks: Ask for “systematic analysis followed by creative solutions”

Chain Prompts for Complex Tasks

Rather than one massive prompt, break complex work into stages:

1. Planning: “Create a detailed outline for…”
2. Execution: “Using this outline, develop section 1…”
3. Refinement: “Review this draft for…”
4. Finalization: “Polish this content to…”

Practice Makes Perfect: LLM Adventure

Reading prompt engineering examples helps, but practice creates mastery. That’s why we built LLM Adventure—a gamified, interactive experience where you apply these principles across 10 progressively challenging levels.

Unlike static tutorials, LLM Adventure provides:

• Immediate feedback on your prompts
• Real-world scenarios from business, technical, and creative domains
• Progressive difficulty that builds skills systematically
• Measurable improvement through score tracking

Teams report 38% improvement in prompt success rates after completing the adventure. The best part? It’s free, requires no signup, and takes just 30 minutes.

Beyond the Basics: Advanced Training

This prompt library provides foundation-level LLM prompts for common scenarios. But enterprise teams often need deeper expertise:

• Custom RAG pipelines for proprietary knowledge bases
• Prompt governance frameworks ensuring consistency across teams
• Advanced techniques like tree-of-thought, self-consistency, and constitutional AI
• Domain-specific optimization for your industry and use cases

Far Horizons’ LLM Residency program embeds our team with yours for 4-6 weeks, combining:

• Hands-on development of production-ready LLM solutions
• Team training through real project work, not classroom theory
• Governance frameworks for responsible AI deployment
• Knowledge transfer ensuring your team maintains and evolves solutions independently

This isn’t consulting where we disappear after delivering a report. It’s systematic capability building that transforms your team’s relationship with AI tools.

Putting Your Prompt Library to Work

You now have 30+ proven prompt examples spanning every major use case. The key to mastery is systematic practice:

Start with templates. Don’t reinvent the wheel—adapt these proven patterns to your specific needs.

Document what works. Build your team’s internal prompt library by saving and sharing successful prompts.

Iterate deliberately. When prompts underperform, analyze why. Too vague? Missing context? Wrong structure?

Measure results. Track which prompts consistently deliver value and which need refinement.

Share knowledge. The teams seeing greatest LLM productivity gains treat prompt engineering as a shared skill, not individual knowledge.

Next Steps

Immediate action: Pick three prompts from this library most relevant to your work. Adapt them to your specific context and test them this week.

Build skills: Complete LLM Adventure to practice these principles with immediate feedback (30 minutes, free, no signup).

Level up your team: If you’re ready to move beyond individual productivity to enterprise-scale LLM capability, explore our LLM Residency program. We’ll embed with your team to build production-ready solutions while transferring systematic prompt engineering expertise.

The frontier of LLM capability isn’t in the models themselves—they’re advancing faster than most organizations can adopt them. The real leverage comes from systematic approaches to prompt engineering that compound over time.

You don’t get transformative results from AI by being a cowboy with prompts. You get there through disciplined, systematic approaches that this prompt library helps you build.

---

About Far Horizons

Far Horizons transforms organizations into systematic innovation powerhouses through disciplined AI and technology adoption. Our proven methodology combines cutting-edge expertise with engineering rigor to deliver solutions that work the first time, scale reliably, and create measurable business impact. Learn more at farhorizons.io.