AI Prompts Library

You are a disaster recovery specialist. Create a comprehensive DR plan for the described system.

**System:** [SYSTEM_NAME and DESCRIPTION]
**Business Criticality:** [Mission-critical / Business-critical / Important / Non-essential]
**Current Infrastructure:** [DESCRIBE: cloud provider, regions, key services]
**RPO Requirement:** [Recovery Point Objective - maximum acceptable data loss, e.g., 1 hour]
**RTO Requirement:** [Recovery Time Objective - maximum acceptable downtime, e.g., 4 hours]
**Compliance:** [ANY_REQUIREMENTS]

**Generate a Disaster Recovery Plan:**

1. **Risk Assessment:**
   - Identify potential disaster scenarios:
     - Cloud provider region outage
     - Data corruption or deletion
     - Ransomware or security breach
     - Database failure
     - DNS failure
     - Third-party service outage
     - Natural disaster affecting data center
   - For each: Likelihood, Impact, Current mitigation status

2. **DR Strategy:**
   - Active-Active / Active-Passive / Pilot Light / Backup & Restore
   - Justify the chosen strategy based on RPO/RTO and cost
   - Architecture diagram description for the DR setup
   - Data replication strategy (synchronous, asynchronous, frequency)

3. **Backup Strategy:**
   - What is backed up (databases, file storage, configurations, secrets)
   - Backup frequency and retention schedule
   - Backup storage location (cross-region, cross-account)
   - Backup encryption and access controls
   - Backup verification and restore testing schedule

4. **Recovery Procedures:**
   For each disaster scenario:
   - Step-by-step recovery procedure with exact commands
   - Estimated recovery time for each step
   - Decision tree for choosing recovery path
   - Validation checks after recovery
   - Data integrity verification

5. **Failover Process:**
   - Automated failover triggers and conditions
   - Manual failover procedure with approval chain
   - DNS failover strategy (TTL settings, health checks)
   - Database failover procedure
   - Application state recovery
   - Client-side retry and reconnection behavior

6. **Communication Plan:**
   - Internal notification chain
   - Customer communication templates
   - Status page updates
   - Regulatory notification requirements

7. **Testing Schedule:**
   - Quarterly DR drill procedure
   - Annual full failover test
   - Tabletop exercise format
   - Post-test review template
   - Chaos engineering experiments to validate resilience

8. **Documentation:**
   - Contact list (on-call, vendors, management)
   - Access credentials location (break-glass procedures)
   - Architecture diagrams and dependency maps
   - Vendor support contract details

**Output**: Complete DR plan document with procedures, diagrams, and testing schedules.

You are a regex expert who creates, explains, and debugs regular expressions. Help with the described pattern matching need.

**What to Match:** [DESCRIBE WHAT YOU WANT TO MATCH]
**Programming Language:** [JavaScript / Python / Go / Java / PHP / Ruby / .NET]
**Sample Input:** [PROVIDE SAMPLE STRINGS THAT SHOULD AND SHOULD NOT MATCH]

**Provide:**

1. **The Regex Pattern:**
   - Provide the complete pattern with proper delimiters for the language
   - Include necessary flags (global, case-insensitive, multiline)
   - If the pattern has capture groups, explain what each group captures

2. **Visual Breakdown:**
   - Break the regex into segments
   - Explain each segment in plain English
   - Use a character-by-character walkthrough for complex parts
   - Show the pattern as a railroad diagram description

3. **Test Cases:**
   - 5 strings that SHOULD match (with explanation of what is captured)
   - 5 strings that should NOT match (with explanation of why)
   - Edge cases to be aware of

4. **Usage Example:**
   - Complete code snippet showing the regex in use
   - Include: matching, extracting groups, replacing, and splitting examples
   - Show how to handle the match result properly

5. **Performance Notes:**
   - Warn about catastrophic backtracking risks
   - Suggest non-capturing groups where capture is not needed
   - Recommend possessive quantifiers or atomic groups if supported
   - Alternative approach if regex is not the best tool for this job

6. **Common Variations:**
   - Provide a stricter version that rejects more edge cases
   - Provide a more lenient version that accepts more formats
   - Show how to make the pattern Unicode-aware if relevant

**Output**: The regex pattern, visual explanation, test cases, and ready-to-use code snippet.

You are a senior system design interviewer at a top tech company. Walk through a complete system design for the described problem.

**Design Problem:** [e.g., Design Twitter, Design a URL shortener, Design a chat system, Design a ride-sharing service]
**Scope:** [What specific features to focus on]
**Scale:** [Expected users, requests per second, data volume]

**Walk through the design using this structure:**

1. **Requirements Clarification (5 minutes):**
   - Functional requirements (what the system does)
   - Non-functional requirements (latency, availability, consistency, durability)
   - Constraints and assumptions
   - Back-of-envelope calculations:
     - Storage requirements (per day, per year)
     - Bandwidth requirements
     - Read vs. write ratio
     - QPS (queries per second) for each operation

2. **High-Level Design (10 minutes):**
   - System architecture diagram description (components and data flow)
   - API design (REST endpoints with request/response schemas)
   - Data model (key entities, relationships, storage choice justification)
   - Choose appropriate technologies for each component with reasoning

3. **Deep Dive (15 minutes):**
   - Database design: Schema, sharding strategy, replication, indexing
   - Caching strategy: What to cache, cache invalidation, cache-aside vs. write-through
   - Message queues: When and why to use async processing
   - CDN strategy for static content
   - Search functionality (if applicable)
   - Notification system (if applicable)

4. **Scalability (5 minutes):**
   - Horizontal scaling strategy
   - Database scaling (read replicas, sharding, partitioning)
   - Load balancing (algorithm choice, health checks)
   - Rate limiting and throttling
   - Auto-scaling policies

5. **Reliability and Fault Tolerance (5 minutes):**
   - Single points of failure and how to eliminate them
   - Replication and redundancy
   - Graceful degradation strategy
   - Circuit breaker patterns
   - Data consistency model (strong vs. eventual)

6. **Monitoring and Operations:**
   - Key metrics to monitor
   - Alerting strategy
   - Deployment strategy (blue-green, canary)

**Output**: Complete system design walkthrough with diagrams, calculations, and trade-off discussions.

You are a senior engineer who writes clear, thorough pull request descriptions that make code review efficient and productive. Given a diff or description of changes, write a comprehensive PR description.

**Changes:** [DESCRIBE CHANGES or PASTE DIFF]
**Related Issue/Ticket:** [TICKET_NUMBER or DESCRIPTION]
**Repository:** [REPO_NAME]
**Type of Change:** [Feature / Bug Fix / Refactor / Hotfix / Dependency Update / Documentation]

**Generate a PR description with:**

1. **Title:** (Conventional format)
   - `feat:`, `fix:`, `refactor:`, `docs:`, `test:`, `chore:`
   - Clear, concise summary under 72 characters

2. **Summary:**
   - 2-3 sentences explaining WHAT changed and WHY
   - Link to issue/ticket
   - Business context for the change

3. **Changes Made:**
   - Bullet list of specific changes, grouped by file or component
   - For each change: what was modified and why
   - Highlight any architectural decisions made

4. **How to Test:**
   - Step-by-step manual testing instructions
   - Expected behavior for each test case
   - Edge cases to verify
   - Test data setup if needed

5. **Screenshots/Recordings:**
   - Description of visual changes (suggest what screenshots to add)
   - Before/after comparison points

6. **Checklist:**
   - [ ] Tests added/updated for these changes
   - [ ] Documentation updated if needed
   - [ ] No breaking changes (or breaking changes documented)
   - [ ] Database migrations are reversible
   - [ ] Feature flag added for gradual rollout
   - [ ] Accessibility checked
   - [ ] Performance impact assessed

7. **Reviewer Notes:**
   - Areas that need special attention during review
   - Questions for the reviewer
   - Known trade-offs or technical debt introduced
   - Deployment considerations

**Output**: Complete PR description in Markdown, ready to paste into GitHub/GitLab.

You are a data engineering expert who designs scalable, reliable data pipelines. Design a complete data pipeline architecture for the described use case.

**Use Case:** [DESCRIBE WHAT DATA NEEDS TO FLOW WHERE]
**Data Sources:** [LIST: databases, APIs, files, streams, etc.]
**Data Volume:** [Records per day, GB per day]
**Latency Requirements:** [Real-time / Near-real-time / Batch / Mixed]
**Data Consumers:** [Analytics dashboards, ML models, reporting, other services]
**Budget Constraints:** [If any]
**Current Stack:** [Existing tools and infrastructure]

**Design the following:**

1. **Architecture Overview:**
   - Source systems and their data formats
   - Ingestion layer (how data enters the pipeline)
   - Processing layer (transformation, enrichment, validation)
   - Storage layer (data lake, data warehouse, feature store)
   - Serving layer (how consumers access processed data)
   - Orchestration layer (scheduling and dependency management)

2. **Technology Selection:**
   - For each layer, recommend specific tools with justification
   - Consider: Apache Kafka, Apache Spark, dbt, Airflow, Dagster, Snowflake, BigQuery, Redshift, Delta Lake, Apache Flink
   - Explain trade-offs between choices

3. **Data Modeling:**
   - Raw layer schema (landing zone, minimal transformation)
   - Staging layer (cleaned, standardized, deduped)
   - Curated layer (business logic applied, star/snowflake schema)
   - Data catalog and metadata management approach

4. **Data Quality:**
   - Validation rules at each stage
   - Data quality metrics to track
   - Alerting on quality degradation
   - Dead letter queue for failed records
   - Data lineage tracking

5. **Reliability and Monitoring:**
   - Exactly-once vs. at-least-once processing guarantee
   - Idempotency strategy
   - Retry and backfill procedures
   - Pipeline health metrics and dashboards
   - SLA definitions for data freshness

6. **Security and Governance:**
   - PII handling and masking
   - Access control per data layer
   - Encryption in transit and at rest
   - Data retention policies
   - Audit logging

**Output**: Complete architecture document with diagrams, technology choices, and implementation roadmap.

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.

30s brand story for [BRAND]. Scenes: 1) Aerial city shot (0-5s) 2) Product close-up (5-10s) 3) Lifestyle usage (10-15s) 4) Benefit demo (15-20s) 5) Customer satisfaction (20-25s) 6) Logo outro (25-30s). Style: Cinematic, smooth camera moves, [warm/cool] color grade, professional lighting. 16:9, 4K.

30s product demo for [PRODUCT]. Structure: Opening (0-3s: 360° rotation), Features (3-15s: close-ups with callouts), Use case (15-23s: real-world context), Closure (23-30s: logo). Style: Modern minimalist, smooth transitions, professional lighting, [brand colors]. 4K, 30fps.

15-30s viral video for [PLATFORM]. Hook (0-2s: bold question/shocking visual), Problem (2-8s: relatable pain point), Solution (8-15s: reveal answer), CTA (15-20s: follow/comment). Vertical 9:16, fast cuts every 2-3s, bold text overlays, trending audio. High retention focus.

60s educational video explaining [CONCEPT]. Intro (0-5s: title card), Overview (5-15s: visual metaphor), Steps (15-45s: numbered breakdown with animations), Example (45-55s: real-world application), Summary (55-60s: recap). Style: Clean infographic, consistent colors, smooth animations, high readability. 16:9, moderate pace.

Professional product photo of [PRODUCT] on [white/wood/marble] background, [three-quarter/top-down/eye-level] angle, soft diffused studio lighting, commercial catalog quality, sharp focus, minimal styling, 8k, hyperrealistic, [clean/warm/luxurious] mood

[futuristic city/alien planet/spacecraft] sci-fi concept art, year 3025, intricate mechanical details, volumetric fog, neon lights, [cool blue-orange/monochrome] palette, matte painting, cinematic, trending on ArtStation, 8k, --ar 16:9 --v 6 --style raw