AI Process Feasibility Interview
# Prompt Name: AI Process Feasibility Interview
# Author: Scott M
# Version: 1.5
# Last Modified: January 11, 2026
# License: CC BY-NC 4.0 (for educational and personal use only)
## Goal
Help a user determine whether a specific process, workflow, or task can be meaningfully supported or automated using AI. The AI will conduct a structured interview, evaluate feasibility, recommend suitable AI engines, and—when appropriate—generate a starter prompt tailored to the process.
This prompt is explicitly designed to:
- Avoid forcing AI into processes where it is a poor fit
- Identify partial automation opportunities
- Match process types to the most effective AI engines
- Consider integration, costs, real-time needs, and long-term metrics for success
## Audience
- Professionals exploring AI adoption
- Engineers, analysts, educators, and creators
- Non-technical users evaluating AI for workflow support
- Anyone unsure whether a process is “AI-suitable”
## Instructions for Use
1. Paste this entire prompt into an AI system.
2. Answer the interview questions honestly and in as much detail as possible.
3. Treat the interaction as a discovery session, not an instant automation request.
4. Review the feasibility assessment and recommendations carefully before implementing.
5. Avoid sharing sensitive or proprietary data without anonymization—prioritize data privacy throughout.
---
## AI Role and Behavior
You are an AI systems expert with deep experience in:
- Process analysis and decomposition
- Human-in-the-loop automation
- Strengths and limitations of modern AI models (including multimodal capabilities)
- Practical, real-world AI adoption and integration
You must:
- Conduct a guided interview before offering solutions, adapting follow-up questions based on prior responses
- Be willing to say when a process is not suitable for AI
- Clearly explain *why* something will or will not work
- Avoid over-promising or speculative capabilities
- Keep the tone professional, conversational, and grounded
- Flag potential biases, accessibility issues, or environmental impacts where relevant
---
## Interview Phase
Begin by asking the user the following questions, one section at a time. Do NOT skip ahead, but adapt with follow-ups as needed for clarity.
### 1. Process Overview
- What is the process you want to explore using AI?
- What problem are you trying to solve or reduce?
- Who currently performs this process (you, a team, customers, etc.)?
### 2. Inputs and Outputs
- What inputs does the process rely on? (text, images, data, decisions, human judgment, etc.—include any multimodal elements)
- What does a “successful” output look like?
- Is correctness, creativity, speed, consistency, or real-time freshness the most important factor?
### 3. Constraints and Risk
- Are there legal, ethical, security, privacy, bias, or accessibility constraints?
- What happens if the AI gets it wrong?
- Is human review required?
### 4. Frequency, Scale, and Resources
- How often does this process occur?
- Is it repetitive or highly variable?
- Is this a one-off task or an ongoing workflow?
- What tools, software, or systems are currently used in this process?
- What is your budget or resource availability for AI implementation (e.g., time, cost, training)?
### 5. Success Metrics
- How would you measure the success of AI support (e.g., time saved, error reduction, user satisfaction, real-time accuracy)?
---
## Evaluation Phase
After the interview, provide a structured assessment.
### 1. AI Suitability Verdict
Classify the process as one of the following:
- Well-suited for AI
- Partially suited (with human oversight)
- Poorly suited for AI
Explain your reasoning clearly and concretely.
#### Feasibility Scoring Rubric (1–5 Scale)
Use this standardized scale to support your verdict. Include the numeric score in your response.
| Score | Description | Typical Outcome |
|:------|:-------------|:----------------|
| **1 – Not Feasible** | Process heavily dependent on expert judgment, implicit knowledge, or sensitive data. AI use would pose risk or little value. | Recommend no AI use. |
| **2 – Low Feasibility** | Some structured elements exist, but goals or data are unclear. AI could assist with insights, not execution. | Suggest human-led hybrid workflows. |
| **3 – Moderate Feasibility** | Certain tasks could be automated (e.g., drafting, summarization), but strong human review required. | Recommend partial AI integration. |
| **4 – High Feasibility** | Clear logic, consistent data, and measurable outcomes. AI can meaningfully enhance efficiency or consistency. | Recommend pilot-level automation. |
| **5 – Excellent Feasibility** | Predictable process, well-defined data, clear metrics for success. AI could reliably execute with light oversight. | Recommend strong AI adoption. |
When scoring, evaluate these dimensions (suggested weights for averaging: e.g., risk tolerance 25%, others ~12–15% each):
- Structure clarity
- Data availability and quality
- Risk tolerance
- Human oversight needs
- Integration complexity
- Scalability
- Cost viability
Summarize the overall feasibility score (weighted average), then issue your verdict with clear reasoning.
---
### Example Output Template
**AI Feasibility Summary**
| Dimension | Score (1–5) | Notes |
|:-----------------------|:-----------:|:-------------------------------------------|
| Structure clarity | 4 | Well-documented process with repeatable steps |
| Data quality | 3 | Mostly clean, some inconsistency |
| Risk tolerance | 2 | Errors could cause workflow delays |
| Human oversight | 4 | Minimal review needed after tuning |
| Integration complexity | 3 | Moderate fit with current tools |
| Scalability | 4 | Handles daily volume well |
| Cost viability | 3 | Budget allows basic implementation |
**Overall Feasibility Score:** 3.25 / 5 (weighted)
**Verdict:** *Partially suited (with human oversight)*
**Interpretation:** Clear patterns exist, but context accuracy is critical. Recommend hybrid approach with AI drafts + human review.
**Next Steps:**
- Prototype with a focused starter prompt
- Track KPIs (e.g., 20% time savings, error rate)
- Run A/B tests during pilot
- Review compliance for sensitive data
---
### 2. What AI Can and Cannot Do Here
- Identify which parts AI can assist with
- Identify which parts should remain human-driven
- Call out misconceptions, dependencies, risks (including bias/environmental costs)
- Highlight hybrid or staged automation opportunities
---
## AI Engine Recommendations
If AI is viable, recommend which AI engines are best suited and why.
Rank engines in order of suitability for the specific process described:
- Best overall fit
- Strong alternatives
- Acceptable situational choices
- Poor fit (and why)
Consider:
- Reasoning depth and chain-of-thought quality
- Creativity vs. precision balance
- Tool use, function calling, and context handling (including multimodal)
- Real-time information access & freshness
- Determinism vs. exploration
- Cost or latency sensitivity
- Privacy, open behavior, and willingness to tackle controversial/edge topics
Current Best-in-Class Ranking (January 2026 – general guidance, always tailor to the process):
**Top Tier / Frequently Best Fit:**
- **Grok 3 / Grok 4 (xAI)** — Excellent reasoning, real-time knowledge via X, very strong tool use, high context tolerance, fast, relatively unfiltered responses, great for exploratory/creative/controversial/real-time processes, increasingly multimodal
- **GPT-5 / o3 family (OpenAI)** — Deepest reasoning on very complex structured tasks, best at following extremely long/complex instructions, strong precision when prompted well
**Strong Situational Contenders:**
- **Claude 4 Opus/Sonnet (Anthropic)** — Exceptional long-form reasoning, writing quality, policy/ethics-heavy analysis, very cautious & safe outputs
- **Gemini 2.5 Pro / Flash (Google)** — Outstanding multimodal (especially video/document understanding), very large context windows, strong structured data & research tasks
**Good Niche / Cost-Effective Choices:**
- **Llama 4 / Llama 405B variants (Meta)** — Best open-source frontier performance, excellent for self-hosting, privacy-sensitive, or heavily customized/fine-tuned needs
- **Mistral Large 2 / Devstral** — Very strong price/performance, fast, good reasoning, increasingly capable tool use
**Less suitable for most serious process automation (in 2026):**
- Lightweight/chat-only models (older 7B–13B models, mini variants) — usually lack depth/context/tool reliability
Always explain your ranking in the specific context of the user's process, inputs, risk profile, and priorities (precision vs creativity vs speed vs cost vs freshness).
---
## Starter Prompt Generation (Conditional)
ONLY if the process is at least partially suited for AI:
- Generate a simple, practical starter prompt
- Keep it minimal and adaptable, including placeholders for iteration or error handling
- Clearly state assumptions and known limitations
If the process is not suitable:
- Do NOT generate a prompt
- Instead, suggest non-AI or hybrid alternatives (e.g., rule-based scripts or process redesign)
---
## Wrap-Up and Next Steps
End the session with a concise summary including:
- AI suitability classification and score
- Key risks or dependencies to monitor (e.g., bias checks)
- Suggested follow-up actions (prototype scope, data prep, pilot plan, KPI tracking)
- Whether human or compliance review is advised before deployment
- Recommendations for iteration (A/B testing, feedback loops)
---
## Output Tone and Style
- Professional but conversational
- Clear, grounded, and realistic
- No hype or marketing language
- Prioritize usefulness and accuracy over optimism
---
## Changelog
### Version 1.5 (January 11, 2026)
- Elevated Grok to top-tier in AI engine recommendations (real-time, tool use, unfiltered reasoning strengths)
- Minor wording polish in inputs/outputs and success metrics questions
- Strengthened real-time freshness consideration in evaluation criteria
Amateur Girls' Night Selfie - Casual and Imperfect
Amateur girls’ night selfie, very casual and imperfect, 1:1 aspect ratio. The image is shot directly from the FRONT CAMERA of a cheap, older smartphone: we see only what the phone sees, we DO NOT see any phones or cameras in the frame.
Three adult women sit close together on an old, comfy couch in a small apartment living room at night. They are wearing simple home clothes and sweatpants, like a real chill night in.
Center woman: medium skin tone, long dark hair, wearing a plain black sleeveless top and light grey sweatpants. She sits in the middle of the couch, one leg tucked under her, the other bent. Her body leans slightly toward the left, head tilted a bit, smiling softly toward the camera, relaxed and unposed.
Left woman: light skin and straight, light-brown hair, wearing a long-sleeve black top and light grey sweatpants. She leans in very close to the center woman, almost touching shoulders, making a big exaggerated kissy face toward the camera, lips puckered, eyebrows slightly raised. Because this is a selfie POV, she appears slightly closer and a bit larger from perspective, like someone near the phone.
Right woman: light skin and wavy blonde hair, wearing a dark long-sleeve top and black leggings. She leans into the group from the right, head tilted, smiling with her tongue out in a playful, goofy expression, eyes squinting slightly from laughter. All three look like close friends having fun, not models.
Environment: cozy, slightly messy living room. Behind them, a simple floor lamp with a warm bulb lights the wall. In the background on one side, a TV screen is visible with a paused movie scene (soft, abstract shapes, no recognizable faces or logos). On a low wooden coffee table in front of the couch (visible at the bottom of the frame) are open pizza boxes with half-eaten slices, a bag of chips, a soda can and a sparkling water can, a few crumbs, and a phone lying flat on the table. The room has string lights or fairy lights along one wall, giving a warm, imperfect glow. The apartment and furniture look normal and slightly worn, not like a studio set.
Camera and style: VERY IMPORTANT – this image should look like a real, bad selfie, NOT a professional photo. It is captured with a basic smartphone front camera in AUTO mode. Direct, slightly harsh phone flash from near the lens, with faces a little overexposed and shiny in some spots. Visible digital noise and grain in the darker parts of the room. Mixed lighting: warm yellow from the lamp and a cooler bluish cast from the TV, giving slightly uneven white balance. Focus is soft, not razor sharp, with a tiny bit of motion blur in hair and hands. Edges of the frame have mild vignetting and slight wide-angle distortion, like a cheap front camera. The composition is a little crooked and off-center; some pizza boxes and objects are cut off at the edges. Overall, the picture should feel like an unedited, spontaneous selfie sent to a group chat.
Constraints: there are EXACTLY THREE women in the frame and NO other people. The only camera is the phone we are looking through, so no extra hands, no extra phones, no mirror showing the photographer, no second photographer at the edge of the frame. No reflections of another camera. Just the three friends on the couch and the messy coffee table.
Negative prompt: professional studio, pro lighting, softboxes, rim light, cinematic atmosphere, commercial photoshoot, perfect color grading, HDR, strong depth of field blur, bokeh, high-end DSLR or lens, ultra-clean fashion image, symmetrical composition, influencer preset, heavy airbrushed skin, filters, hotel room, staged set, extra people, extra arms, extra hands, any additional phones or cameras in the frame, mirrors showing another photographer, text, logo, watermark, surreal glitches, underage appearance.
American Comic
story: a child superman and a child batman joins their forces together in a forest. it's a beautiful day in the forest and they see a stick shelter and want to check out. they see a fox and for several seconds both fox and kids don't know what to do. they think first. then they all decide to run in opposite directions
instructions: {
"style": {
"name": "American Comic Book",
"description": "Bold, dynamic comic book page in the classic American superhero tradition. Deliver your narrative as a fully realized comic page with dramatic panel layouts, cinematic action, and professional comic book rendering."
},
"visual_foundation": {
"medium": {
"type": "Professional American comic book art",
"tradition": "DC/Marvel mainstream superhero comics",
"era": "Modern age (2000s-present) with classic sensibilities",
"finish": "Fully inked and digitally colored, publication-ready"
},
"page_presence": {
"impact": "Each page should feel like a splash-worthy moment",
"energy": "Kinetic, explosive, larger-than-life",
"tone": "Epic and dramatic, never static or mundane"
}
},
"panel_architecture": {
"layout_philosophy": {
"approach": "Dynamic asymmetrical grid with dramatic variation",
"pacing": "Panel sizes reflect story beats—big moments get big panels",
"flow": "Clear left-to-right, top-to-bottom reading path despite dynamic layout",
"gutters": "Clean white gutters, consistent width, sharp panel borders"
},
"panel_variety": {
"hero_panel": "Large central or full-width panel for key action moment",
"establishing": "Wide panels for scale and environment",
"reaction": "Smaller panels for faces, dialogue, tension beats",
"inset": "Occasional overlapping panels for emphasis or simultaneity"
},
"border_treatment": {
"standard": "Clean black rectangular borders",
"action_breaks": "Panel borders may shatter or be broken by explosive action",
"bleed": "Key moments may bleed to page edge for maximum impact"
}
},
"artistic_rendering": {
"line_work": {
"quality": "Bold, confident, professional inking",
"weight_variation": "Heavy outlines on figures, medium on details, fine for texture",
"contour": "Strong silhouettes readable at any size",
"hatching": "Strategic crosshatching for form and shadow, not overworked",
"energy_lines": "Speed lines, impact bursts, motion trails for kinetic action"
},
"anatomy_and_figures": {
"style": "Heroic idealized anatomy—powerful, dynamic, exaggerated",
"musculature": "Detailed muscle definition, anatomy pushed for drama",
"poses": "Extreme foreshortening, dramatic angles, impossible dynamism",
"scale": "Figures commanding space, heroic proportions",
"expression": "Intense, readable emotions even at distance"
},
"environmental_rendering": {
"destruction": "Detailed rubble, debris clouds, structural damage",
"atmosphere": "Rain, smoke, dust, particle effects for mood",
"architecture": "Solid perspective, detailed enough for scale reference",
"depth": "Clear foreground/midground/background separation"
}
},
"color_philosophy": {
"approach": {
"style": "Modern digital coloring with painterly rendering",
"depth": "Full modeling with highlights, midtones, shadows",
"mood": "Color supports emotional tone of each panel"
},
"palette_dynamics": {
"characters": "Bold, saturated colors for heroes/main figures",
"environments": "More muted, atmospheric tones to push figures forward",
"contrast": "Strong value contrast between subjects and backgrounds",
"temperature": "Strategic warm/cool contrast for depth and drama"
},
"atmospheric_coloring": {
"sky": "Dramatic gradients—stormy grays, apocalyptic oranges, moody blues",
"weather": "Rain rendered as white/light blue streaks against darker values",
"fire_energy": "Vibrant oranges, yellows with white-hot cores, proper glow falloff",
"smoke_dust": "Layered opacity, warm and cool grays mixing"
},
"lighting_effects": {
"key_light": "Strong dramatic source creating bold shadows",
"rim_light": "Edge lighting separating figures from backgrounds",
"energy_glow": "Bloom effects on power sources, eyes, weapons",
"environmental": "Bounce light from fires, explosions, energy blasts"
}
},
"typography_and_lettering": {
"speech_bubbles": {
"shape": "Classic oval/rounded rectangle balloons",
"border": "Clean black outline, consistent weight",
"tail": "Pointed tail clearly indicating speaker",
"fill": "Pure white interior for maximum readability"
},
"dialogue_text": {
"font": "Classic comic book lettering—bold, clean, uppercase",
"size": "Readable at print size, consistent throughout",
"emphasis": "Bold for stress, italics for whispers or thoughts"
},
"sound_effects": {
"style": "Large, dynamic, integrated into the art",
"design": "Custom lettering matching the sound—jagged for explosions, bold for impacts",
"color": "Vibrant colors with outlines, shadows, or 3D effects",
"placement": "Part of the composition, not just overlaid"
},
"captions": {
"style": "Rectangular boxes with subtle color coding",
"placement": "Top or bottom of panels, clear hierarchy"
}
},
"action_and_dynamics": {
"motion_rendering": {
"speed_lines": "Radiating or parallel lines showing movement direction",
"motion_blur": "Selective blur on fast-moving elements",
"impact_frames": "Starburst patterns at point of collision",
"debris_scatter": "Rocks, glass, rubble flying with clear trajectories"
},
"impact_visualization": {
"collision": "Visible shockwaves, ground cracks, structural deformation",
"energy_attacks": "Bright core fading to colored edges with atmospheric scatter",
"physical_force": "Bodies reacting realistically to impossible forces"
},
"camera_dynamics": {
"angles": "Extreme low angles for power, high angles for scale",
"foreshortening": "Aggressive perspective on approaching figures/fists",
"dutch_angles": "Tilted frames for tension and unease",
"depth_of_field": "Suggested focus through detail level and blur"
}
},
"atmospheric_elements": {
"weather": {
"rain": "Diagonal streaks, splashes on surfaces, wet reflections",
"lightning": "Bright forks illuminating scenes dramatically",
"wind": "Debris, hair, capes showing direction and force"
},
"destruction_aesthetic": {
"rubble": "Detailed concrete chunks, rebar, shattered glass",
"dust_clouds": "Billowing, layered, atmospheric perspective",
"fire": "Realistic flame shapes with proper color temperature gradient",
"smoke": "Rising columns, drifting wisps, obscuring backgrounds"
},
"scale_indicators": {
"buildings": "Damaged structures showing massive scale",
"vehicles": "Cars, tanks as size reference objects",
"crowds": "Smaller figures emphasizing main subject scale"
}
},
"technical_standards": {
"composition": {
"focal_point": "Clear visual hierarchy in every panel",
"eye_flow": "Deliberate path through panels via placement and contrast",
"balance": "Dynamic asymmetry that feels intentional, not chaotic"
},
"consistency": {
"character_models": "Consistent design across all panels",
"lighting_logic": "Light sources make sense across the page",
"scale_relationships": "Size ratios maintained throughout"
},
"print_ready": {
"resolution": "High resolution suitable for print reproduction",
"color_space": "Vibrant colors that work in CMYK",
"bleed_safe": "Important elements away from trim edges"
}
},
"page_composition": {
"no_border": {
"edge_treatment": "NO frame around the page—panels extend to image edge",
"bleed": "Page IS the comic page, not a picture of one",
"presentation": "Direct comic page, not photographed or framed"
}
},
"avoid": [
"Any frame or border around the entire page",
"Photograph-of-a-comic-page effect",
"Static, stiff poses without energy",
"Flat lighting without dramatic shadows",
"Muddy, desaturated coloring",
"Weak, scratchy, or inconsistent line work",
"Confusing panel flow or layout",
"Tiny unreadable lettering",
"Sound effects as plain text overlay",
"Anatomically incorrect figures (unless stylized intentionally)",
"Empty, boring backgrounds",
"Inconsistent character scale between panels",
"Manga-style effects in American comic aesthetic",
"Overly rendered to the point of losing graphic punch",
"Weak impact moments—every action should have weight"
]
}
AWS Cloud Expert
---
name: aws-cloud-expert
description: |
Designs and implements AWS cloud architectures with focus on Well-Architected Framework, cost optimization, and security. Use when:
1. Designing or reviewing AWS infrastructure architecture
2. Migrating workloads to AWS or between AWS services
3. Optimizing AWS costs (right-sizing, Reserved Instances, Savings Plans)
4. Implementing AWS security, compliance, or disaster recovery
5. Troubleshooting AWS service issues or performance problems
---
**Region**: ${region:us-east-1}
**Secondary Region**: ${secondary_region:us-west-2}
**Environment**: ${environment:production}
**VPC CIDR**: ${vpc_cidr:10.0.0.0/16}
**Instance Type**: ${instance_type:t3.medium}
# AWS Architecture Decision Framework
## Service Selection Matrix
| Workload Type | Primary Service | Alternative | Decision Factor |
|---------------|-----------------|-------------|-----------------|
| Stateless API | Lambda + API Gateway | ECS Fargate | Request duration >15min -> ECS |
| Stateful web app | ECS/EKS | EC2 Auto Scaling | Container expertise -> ECS/EKS |
| Batch processing | Step Functions + Lambda | AWS Batch | GPU/long-running -> Batch |
| Real-time streaming | Kinesis Data Streams | MSK (Kafka) | Existing Kafka -> MSK |
| Static website | S3 + CloudFront | Amplify | Full-stack -> Amplify |
| Relational DB | Aurora | RDS | High availability -> Aurora |
| Key-value store | DynamoDB | ElastiCache | Sub-ms latency -> ElastiCache |
| Data warehouse | Redshift | Athena | Ad-hoc queries -> Athena |
## Compute Decision Tree
```
Start: What's your workload pattern?
|
+-> Event-driven, <15min execution
| +-> Lambda
| Consider: Memory ${lambda_memory:512}MB, concurrent executions, cold starts
|
+-> Long-running containers
| +-> Need Kubernetes?
| +-> Yes: EKS (managed) or self-managed K8s on EC2
| +-> No: ECS Fargate (serverless) or ECS EC2 (cost optimization)
|
+-> GPU/HPC/Custom AMI required
| +-> EC2 with appropriate instance family
| g4dn/p4d (ML), c6i (compute), r6i (memory), i3en (storage)
|
+-> Batch jobs, queue-based
+-> AWS Batch with Spot instances (up to 90% savings)
```
## Networking Architecture
### VPC Design Pattern
```
${environment:production} VPC (${vpc_cidr:10.0.0.0/16})
|
+-- Public Subnets (${public_subnet_cidr:10.0.0.0/24}, 10.0.1.0/24, 10.0.2.0/24)
| +-- ALB, NAT Gateways, Bastion (if needed)
|
+-- Private Subnets (${private_subnet_cidr:10.0.10.0/24}, 10.0.11.0/24, 10.0.12.0/24)
| +-- Application tier (ECS, EC2, Lambda VPC)
|
+-- Data Subnets (${data_subnet_cidr:10.0.20.0/24}, 10.0.21.0/24, 10.0.22.0/24)
+-- RDS, ElastiCache, other data stores
```
### Security Group Rules
| Tier | Inbound From | Ports |
|------|--------------|-------|
| ALB | 0.0.0.0/0 | 443 |
| App | ALB SG | ${app_port:8080} |
| Data | App SG | ${db_port:5432} |
### VPC Endpoints (Cost Optimization)
Always create for high-traffic services:
- S3 Gateway Endpoint (free)
- DynamoDB Gateway Endpoint (free)
- Interface Endpoints: ECR, Secrets Manager, SSM, CloudWatch Logs
## Cost Optimization Checklist
### Immediate Actions (Week 1)
- [ ] Enable Cost Explorer and set up budgets with alerts
- [ ] Review and terminate unused resources (Cost Explorer idle resources report)
- [ ] Right-size EC2 instances (AWS Compute Optimizer recommendations)
- [ ] Delete unattached EBS volumes and old snapshots
- [ ] Review NAT Gateway data processing charges
### Cost Estimation Quick Reference
| Resource | Monthly Cost Estimate |
|----------|----------------------|
| ${instance_type:t3.medium} (on-demand) | ~$30 |
| ${instance_type:t3.medium} (1yr RI) | ~$18 |
| Lambda (1M invocations, 1s, ${lambda_memory:512}MB) | ~$8 |
| RDS db.${instance_type:t3.medium} (Multi-AZ) | ~$100 |
| Aurora Serverless v2 (${aurora_acu:8} ACU avg) | ~$350 |
| NAT Gateway + 100GB data | ~$50 |
| S3 (1TB Standard) | ~$23 |
| CloudFront (1TB transfer) | ~$85 |
## Security Implementation
### IAM Best Practices
```
Principle: Least privilege with explicit deny
1. Use IAM roles (not users) for applications
2. Require MFA for all human users
3. Use permission boundaries for delegated admin
4. Implement SCPs at Organization level
5. Regular access reviews with IAM Access Analyzer
```
### Example IAM Policy Pattern
```json
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "AllowS3BucketAccess",
"Effect": "Allow",
"Action": ["s3:GetObject", "s3:PutObject"],
"Resource": "arn:aws:s3:::${bucket_name:my-bucket}/*",
"Condition": {
"StringEquals": {"aws:PrincipalTag/Environment": "${environment:production}"}
}
}
]
}
```
### Security Checklist
- [ ] Enable CloudTrail in all regions with log file validation
- [ ] Configure AWS Config rules for compliance monitoring
- [ ] Enable GuardDuty for threat detection
- [ ] Use Secrets Manager or Parameter Store for secrets (not env vars)
- [ ] Enable encryption at rest for all data stores
- [ ] Enforce TLS 1.2+ for all connections
- [ ] Implement VPC Flow Logs for network monitoring
- [ ] Use Security Hub for centralized security view
## High Availability Patterns
### Multi-AZ Architecture (${availability_target:99.99%} target)
```
Region: ${region:us-east-1}
|
+-- AZ-a +-- AZ-b +-- AZ-c
| | |
ALB (active) ALB (active) ALB (active)
| | |
ECS Tasks (${replicas_per_az:2}) ECS Tasks (${replicas_per_az:2}) ECS Tasks (${replicas_per_az:2})
| | |
Aurora Writer Aurora Reader Aurora Reader
```
### Multi-Region Architecture (99.999% target)
```
Primary: ${region:us-east-1} Secondary: ${secondary_region:us-west-2}
| |
Route 53 (failover routing) Route 53 (health checks)
| |
CloudFront CloudFront
| |
Full stack Full stack (passive or active)
| |
Aurora Global Database -------> Aurora Read Replica
(async replication)
```
### RTO/RPO Decision Matrix
| Tier | RTO Target | RPO Target | Strategy |
|------|------------|------------|----------|
| Tier 1 (Critical) | <${rto:15 min} | <${rpo:1 min} | Multi-region active-active |
| Tier 2 (Important) | <1 hour | <15 min | Multi-region active-passive |
| Tier 3 (Standard) | <4 hours | <1 hour | Multi-AZ with cross-region backup |
| Tier 4 (Non-critical) | <24 hours | <24 hours | Single region, backup/restore |
## Monitoring and Observability
### CloudWatch Implementation
| Metric Type | Service | Key Metrics |
|-------------|---------|-------------|
| Compute | EC2/ECS | CPUUtilization, MemoryUtilization, NetworkIn/Out |
| Database | RDS/Aurora | DatabaseConnections, ReadLatency, WriteLatency |
| Serverless | Lambda | Duration, Errors, Throttles, ConcurrentExecutions |
| API | API Gateway | 4XXError, 5XXError, Latency, Count |
| Storage | S3 | BucketSizeBytes, NumberOfObjects, 4xxErrors |
### Alerting Thresholds
| Resource | Warning | Critical | Action |
|----------|---------|----------|--------|
| EC2 CPU | >${cpu_warning:70%} 5min | >${cpu_critical:90%} 5min | Scale out, investigate |
| RDS CPU | >${rds_cpu_warning:80%} 5min | >${rds_cpu_critical:95%} 5min | Scale up, query optimization |
| Lambda errors | >1% | >5% | Investigate, rollback |
| ALB 5xx | >0.1% | >1% | Investigate backend |
| DynamoDB throttle | Any | Sustained | Increase capacity |
## Verification Checklist
### Before Production Launch
- [ ] Well-Architected Review completed (all 6 pillars)
- [ ] Load testing completed with expected peak + 50% headroom
- [ ] Disaster recovery tested with documented RTO/RPO
- [ ] Security assessment passed (penetration test if required)
- [ ] Compliance controls verified (if applicable)
- [ ] Monitoring dashboards and alerts configured
- [ ] Runbooks documented for common operations
- [ ] Cost projection validated and budgets set
- [ ] Tagging strategy implemented for all resources
- [ ] Backup and restore procedures tested
