AI Prompts Library

Profile this code for performance issues:

[PASTE CODE]

1. Identify the top 3 performance bottlenecks. For each, explain WHY it's slow (Big-O, IO blocking, repeated allocations, etc.).
2. Propose a specific optimization for each.
3. Estimate the speedup (rough order of magnitude).
4. Identify the lowest-hanging fruit - the optimization with best ROI (largest speedup per line of code change).
5. Provide the optimized code only for the top recommendation. Don't refactor everything at once.

If the code is already efficient, say so honestly.

You are a database performance expert specializing in SQL query optimization. Analyze the provided query and suggest improvements for maximum performance.

**Query to Optimize:**
```sql
[PASTE YOUR SQL QUERY HERE]
```

**Database:** [PostgreSQL / MySQL / SQL Server / SQLite]
**Table Size:** [Approximate row counts for each table involved]
**Current Execution Time:** [If known]
**Available Indexes:** [List existing indexes, or say 'unknown']

**Optimization Analysis:**

1. **Query Plan Analysis:**
   - Identify sequential scans that should be index scans
   - Detect sort operations that could be avoided with indexes
   - Find hash joins that could be merge joins or nested loops
   - Spot unnecessary subqueries or CTEs

2. **Index Recommendations:**
   - Covering indexes for the most common queries
   - Composite indexes (correct column order for the query pattern)
   - Partial indexes for queries with WHERE filters
   - Expression indexes for computed conditions
   - Include CONCURRENTLY option for production index creation

3. **Query Rewriting:**
   - Replace correlated subqueries with JOINs
   - Convert IN (SELECT ...) to EXISTS where appropriate
   - Use window functions instead of self-joins
   - Replace DISTINCT with GROUP BY when more efficient
   - Optimize pagination (keyset pagination vs OFFSET)
   - Use UNION ALL instead of UNION when duplicates are impossible

4. **Schema Suggestions:**
   - Denormalization opportunities for read-heavy queries
   - Materialized views for complex aggregations
   - Partitioning strategy for large tables
   - Data type optimizations (e.g., INT vs BIGINT, VARCHAR vs TEXT)

5. **Optimized Query:**
   - Provide the rewritten query
   - Include EXPLAIN ANALYZE output comparison notes
   - Estimate the performance improvement

**Output**: The optimized query, required index DDL statements, and a brief explanation of each change and its expected impact.

You are a conversion rate optimization (CRO) expert. Generate structured A/B test hypotheses for the described page or funnel.

**Page/Funnel to Optimize:** [URL or description]
**Current Conversion Rate:** [X%]
**Traffic Volume:** [Monthly visitors]
**Primary Conversion Goal:** [Sign up / Purchase / Click / Download]
**Known Pain Points:** [User feedback, heatmap insights, analytics data]

**For each hypothesis, provide:**

1. **Hypothesis Statement** (use this format):
   "If we [CHANGE], then [METRIC] will [INCREASE/DECREASE] by [ESTIMATED %], because [REASONING BASED ON DATA/PSYCHOLOGY]."

2. **Test Details:**
   - Element to change: [Button, headline, layout, form, pricing, image, copy]
   - Control (A): Current version description
   - Variant (B): Proposed change description
   - Variant (C): Alternative change (optional)

3. **Prioritization Score** (ICE Framework):
   - Impact (1-10): How much improvement if the hypothesis is correct
   - Confidence (1-10): How confident are you based on data and best practices
   - Ease (1-10): How easy is it to implement
   - ICE Score: Impact x Confidence x Ease

4. **Statistical Requirements:**
   - Minimum sample size per variant
   - Estimated test duration based on traffic
   - Minimum detectable effect (MDE)
   - Statistical significance threshold (typically 95%)

5. **Behavioral Psychology Principle:**
   - Which principle supports this change (anchoring, social proof, loss aversion, cognitive load reduction, Hick's Law, etc.)

**Generate 10 test hypotheses, ranked by ICE score.** Include a mix of quick wins (high ease) and high-impact changes. For each, explain the behavioral science principle at work.

**Output**: Prioritized list ready to load into an A/B testing tool (Optimizely, VWO, Google Optimize).

You are a prompt engineering expert who helps people write more effective prompts for AI models. Given a task description, generate an optimized prompt that will produce the best results.

**Task:** [DESCRIBE WHAT YOU WANT THE AI TO DO]
**Target AI Model:** [GPT-4 / Claude / Gemini / Llama / General]
**Output Format:** [Text / JSON / Code / Markdown / Structured data]
**Quality Level:** [Draft / Professional / Publication-ready]

**Prompt Engineering Principles to Apply:**

1. **Role Assignment:**
   - Assign a specific expert persona that matches the task
   - Include years of experience and domain expertise
   - Specify the audience they communicate with

2. **Context Setting:**
   - Provide all necessary background information
   - Define the scope (what to include and exclude)
   - Set constraints and boundaries

3. **Task Specification:**
   - Use clear, unambiguous instructions
   - Break complex tasks into numbered steps
   - Specify the output format exactly
   - Include examples of desired output (few-shot prompting)

4. **Quality Controls:**
   - Define what "good" looks like (evaluation criteria)
   - Specify what to avoid (common mistakes, anti-patterns)
   - Request self-verification steps
   - Ask for confidence levels on uncertain items

5. **Advanced Techniques:**
   - Chain-of-thought: Ask the model to reason step by step
   - Self-consistency: Request multiple approaches and compare
   - Structured output: Use templates and schemas
   - Iterative refinement: Build in revision cycles

6. **Output Formatting:**
   - Specify headings, bullet points, code blocks
   - Define length constraints (word count, page count)
   - Request metadata (confidence, sources, alternatives)

**Generate:**
- The optimized prompt
- Explanation of each technique used and why
- 3 variations for different emphasis (concise, detailed, creative)
- Tips for iterating on the prompt based on initial results

**Output**: The optimized prompt plus a brief guide on how to use and refine it.

Optimize Dockerfile:

**Multi-stage Build** - Builder + production stages
**Layer Caching** - Dependencies first, code last
**Security** - Non-root user, minimal base image
**Size Reduction** - Alpine Linux, .dockerignore
**Performance** - Parallel builds, BuildKit

Include: Optimized Dockerfile, .dockerignore, docker-compose.yml, build commands, size comparison.

YouTube optimization:

**Title** (60 chars) - Keyword-rich, click-worthy, numbers work
**Description** - Hook in first 150 chars, timestamps, keywords, links
**Tags** - Primary keyword, variations, long-tail, branded
**Thumbnail** - 1280x720, clear focal point, contrasting colors, readable text
**Engagement**: Hook first 15s, mid-roll CTAs, end screens, pinned comment
**Playlists**: Relevant series organization

Maximize discoverability.

Create structured A/B test hypothesis:

**Test Subject**: [PAGE/FEATURE]
**Current Metric**: [VALUE]

**Hypothesis**: If [CHANGE] then [EXPECTED OUTCOME] because [REASONING]

**Control (A)**: Current version
**Variant (B)**: Modified version with [SPECIFIC CHANGES]

**Success Metrics**:
- Primary: [METRIC]
- Secondary: [METRICS]

**Sample Size**: Calculate with 95% confidence, 80% power
**Duration**: Minimum [X] days
**Segment**: [ALL/SPECIFIC]

**Risks**: [POTENTIAL ISSUES]
**Rollback Plan**: [IF NEGATIVE IMPACT]

Optimize [WEB APP/API/DATABASE] performance:

**Frontend Optimization**:
- [ ] Minify CSS/JS/HTML
- [ ] Compress images (WebP, lazy loading)
- [ ] Use CDN for static assets
- [ ] Implement code splitting
- [ ] Add browser caching headers
- [ ] Reduce HTTP requests
- [ ] Optimize fonts (font-display: swap)

**Backend Optimization**:
- [ ] Database query optimization
- [ ] Add database indexes
- [ ] Implement caching (Redis/Memcached)
- [ ] Use connection pooling
- [ ] Add rate limiting
- [ ] Optimize API responses (pagination, partial)
- [ ] Enable gzip compression

**Database Optimization**:
- [ ] Analyze slow queries (EXPLAIN)
- [ ] Add composite indexes
- [ ] Denormalize where beneficial
- [ ] Implement query caching
- [ ] Optimize table structure
- [ ] Regular VACUUM/ANALYZE

**Monitoring**:
- [ ] Set up APM (New Relic, DataDog)
- [ ] Track Core Web Vitals
- [ ] Monitor error rates
- [ ] Set performance budgets

Analyze & optimize conversion funnel:

**Funnel Stages**:
1. **Awareness**: [TRAFFIC SOURCES]
   - Current: [X visitors/month]
   - Drop-off: [%]
   
2. **Interest**: [LANDING PAGE]
   - Current: [X engaged]
   - Drop-off: [%]
   - Key metric: Time on page, scroll depth
   
3. **Consideration**: [PRODUCT/PRICING PAGE]
   - Current: [X viewers]
   - Drop-off: [%]
   - Key metric: Page views, comparison clicks
   
4. **Intent**: [CART/SIGNUP]
   - Current: [X initiated]
   - Drop-off: [%]
   - Key metric: Form starts
   
5. **Conversion**: [PURCHASE/SIGNUP]
   - Current: [X completed]
   - Conversion rate: [%]

**Analysis Questions**:
- Where is the biggest drop-off?
- What's the average time between stages?
- Which traffic sources convert best?
- What devices/browsers have issues?
- Are there technical errors?

**Optimization Tactics**:

**Awareness**:
- Improve targeting
- A/B test ad copy
- Optimize SEO

**Interest**:
- Strengthen value prop
- Improve page speed
- Add trust signals
- Better CTA placement

**Consideration**:
- Clear pricing
- Add comparison tables
- Show social proof
- Address objections

**Intent**:
- Simplify forms
- Show progress
- Remove distractions
- Add live chat

**Conversion**:
- Multiple payment options
- Guest checkout
- Security badges
- Confirmation clarity

**Metrics to Track**:
- Overall conversion rate
- Stage conversion rates
- Time to convert
- Revenue per visitor
- Cart abandonment rate

**Tools**: Google Analytics, Hotjar, Mixpanel, Amplitude

Analyze and optimize the following content for SEO, focusing on both search engines and user experience.

Target Keyword: [PRIMARY KEYWORD]
Secondary Keywords: [KEYWORD 1, KEYWORD 2, KEYWORD 3]
Target Audience: [AUDIENCE DESCRIPTION]
Search Intent: [Informational/Transactional/Navigational/Commercial]

**Content to Optimize:**
[PASTE CONTENT HERE]

**Provide:**

1. **SEO Analysis**
   - Keyword density (primary and secondary)
   - Readability score and improvements
   - Meta title and description recommendations
   - Header structure (H1, H2, H3) analysis
   - Internal/external link opportunities

2. **Content Improvements**
   - Rewrite weak sections
   - Add semantic keywords and LSI terms
   - Improve opening paragraph (hook)
   - Enhance calls-to-action
   - Add FAQ schema markup suggestions

3. **Technical SEO**
   - Image alt text recommendations
   - URL slug suggestion
   - Schema markup opportunities
   - Mobile optimization notes

4. **Competitive Analysis**
   - Missing topics covered by top-ranking pages
   - Content gaps to fill
   - Unique angle suggestions

5. **Final Optimized Version**
   - Complete rewritten content incorporating all improvements

Analyze and optimize the following SQL query for maximum performance.

**Current Query:**
```sql
[PASTE YOUR SQL QUERY HERE]
```

**Database Context:**
- Database System: [MySQL/PostgreSQL/SQL Server/Oracle]
- Table row counts: [TABLE1: X rows, TABLE2: Y rows]
- Current indexes: [LIST EXISTING INDEXES]
- Query execution time: [CURRENT TIME]
- Use case: [DESCRIPTION OF WHAT QUERY DOES]

**Performance Requirements:**
- Target execution time: [GOAL]
- Frequency: [How often this query runs]
- Data freshness needs: [Real-time/Near real-time/Can be cached]

**Please provide:**

1. **Performance Analysis**
   - Identify performance bottlenecks
   - Explain current query execution plan
   - Point out inefficient operations (full table scans, cartesian products, etc.)

2. **Optimized Query**
   - Rewritten query with improvements
   - Inline comments explaining changes
   - Alternative approaches if multiple solutions exist

3. **Indexing Strategy**
   - Recommended indexes to create
   - Explain which clauses each index helps
   - Consider trade-offs (write performance, storage)

4. **Additional Optimizations**
   - Partitioning recommendations
   - Caching opportunities
   - Database configuration tweaks
   - Query refactoring into multiple queries if beneficial

5. **Expected Performance Gain**
   - Estimated improvement
   - Explain why these changes help

6. **Monitoring & Maintenance**
   - What metrics to track
   - When to revisit the optimization