577 lines
12 KiB
Markdown
577 lines
12 KiB
Markdown
---
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description: Generate research prompts for technical decisions
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---
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# BMAD Research - Generate Research Prompts
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You are helping the user research technical decisions by generating comprehensive research prompts for web-based AI (ChatGPT, Claude web) which have web search capabilities.
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## Purpose
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Generate structured research prompts that users can copy to ChatGPT/Claude web to research:
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- API vendors and data sources
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- Authentication providers
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- Hosting platforms
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- Payment processors
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- Third-party integrations
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- Technology stack options
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Results are documented in structured templates and referenced during architecture generation.
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## When to Use
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**During BMAD workflow**:
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- After PRD mentions external APIs/vendors
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- Before architecture generation
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- When technical decisions need research
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**Standalone**:
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- Evaluating vendor options
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- Comparing technologies
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- Cost analysis
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- Technical due diligence
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## Process
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### Step 1: Identify Research Topic
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**If user provided topic**:
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```bash
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# User ran: /bmad:research "data vendors for precious metals"
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```
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- Topic = "data vendors for precious metals"
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**If no topic**:
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- Ask: "What do you need to research?"
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- Show common topics:
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```
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Common research topics:
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1. Data vendors/APIs
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2. Hosting platforms (Railway, Vercel, GCP, etc.)
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3. Authentication providers (Clerk, Auth0, custom, etc.)
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4. Payment processors (Stripe, PayPal, etc.)
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5. AI/ML options (OpenAI, Anthropic, self-hosted)
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6. Database options
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7. Other (specify)
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Topic:
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```
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### Step 2: Gather Context from PRD
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**If PRD exists**:
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```bash
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Read bmad-backlog/prd/prd.md
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```
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Extract relevant context:
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- What features need this research?
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- What are the constraints? (budget, performance)
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- Any technical preferences mentioned?
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**If no PRD**:
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- Use topic only
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- Generate generic research prompt
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- Note: "Research will be more focused with a PRD"
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### Step 3: Generate Research Prompt
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Create comprehensive prompt for web AI.
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**Topic slug**: Convert topic to filename-safe string
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```python
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topic_slug = topic.lower().replace(' ', '-').replace('/', '-')
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# "data vendors for precious metals" → "data-vendors-for-precious-metals"
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```
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**Save to**: `bmad-backlog/research/RESEARCH-{topic_slug}-prompt.md`
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**Prompt content**:
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```markdown
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# Research Prompt: {Topic}
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**COPY THIS ENTIRE PROMPT** and paste into ChatGPT (GPT-4) or Claude (web).
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They have web search and can provide current, comprehensive research.
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---
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## Research Request
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**Project**: {{project name from PRD or "New Project"}}
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**Research Topic**: {{topic}}
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**Context**:
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{{Extract from PRD:
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- What features need this
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- Performance requirements
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- Budget constraints
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- Technical preferences}}
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---
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## What I Need
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Please research and provide:
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### 1. Overview
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- What options exist for {{topic}}?
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- What are the top 5-7 solutions/vendors/APIs?
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- Current market leaders?
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### 2. Comparison Table
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Create a detailed comparison table:
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| Option | Pricing | Key Features | Pros | Cons | Best For |
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|--------|---------|--------------|------|------|----------|
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| Option 1 | | | | | |
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| Option 2 | | | | | |
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| Option 3 | | | | | |
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### 3. Technical Details
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For each option, provide:
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- **API Documentation**: Official docs link
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- **Authentication**: API key, OAuth, etc.
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- **Rate Limits**: Requests per minute/hour
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- **Data Format**: JSON, XML, GraphQL, etc.
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- **SDKs**: Python, Node.js, etc. with links
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- **Code Examples**: If available
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- **Community**: GitHub stars, Stack Overflow activity
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### 4. Integration Complexity
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For each option:
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- **Estimated Setup Time**: Hours/days
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- **Dependencies**: What else is needed
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- **Learning Curve**: Easy/Medium/Hard
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- **Documentation Quality**: Excellent/Good/Poor
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- **Community Support**: Active/Moderate/Limited
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### 5. Recommendations
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Based on my project requirements:
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{{List key requirements}}
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Which option would you recommend and why?
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Provide recommendation for:
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- **MVP**: Best for getting started quickly
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- **Production**: Best for long-term reliability
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- **Budget**: Most cost-effective option
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### 6. Cost Analysis
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For each option, provide:
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**Free Tier**:
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- What's included
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- Limitations
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- Good for MVP? (yes/no)
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**Paid Tiers**:
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- Tier names and pricing
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- What each tier includes
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- Rate limit increases
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**Estimated Monthly Cost**:
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- MVP (low volume): $X-Y
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- Production (medium volume): $X-Y
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- Scale (high volume): $X-Y
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### 7. Risks & Considerations
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For each option:
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- **Vendor Lock-in**: How easy to migrate away?
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- **Data Quality**: Accuracy, freshness, reliability
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- **Compliance**: Regional restrictions, data governance
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- **Uptime/SLA**: Published SLAs, historical uptime
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- **Support**: Response times, support channels
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### 8. Source Links
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Provide links to:
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- Official website
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- Pricing page
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- API documentation
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- Getting started guide
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- Community forums/Discord
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- Comparison articles/reviews
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- GitHub repositories (if applicable)
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---
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## Deliverable Format
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Please structure your response to match the sections above for easy copy/paste into my findings template.
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Thank you!
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```
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**Write this to file**: bmad-backlog/research/RESEARCH-{topic_slug}-prompt.md
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### Step 4: Generate Findings Template
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Create structured template for documenting research.
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**Save to**: `bmad-backlog/research/RESEARCH-{topic_slug}-findings.md`
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**Template content**:
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```markdown
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# Research Findings: {Topic}
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**Date**: {current date}
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**Researcher**: {user name or TBD}
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**Status**: Draft
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---
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## Research Summary
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**Question**: {what was researched}
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**Recommendation**: {chosen option and why}
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**Confidence**: High | Medium | Low
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---
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## Options Evaluated
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### Option 1: {Name}
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**Overview**:
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**Pricing**:
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- Free tier:
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- Paid tiers:
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- Estimated cost for MVP: $X/month
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- Estimated cost for Production: $Y/month
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**Features**:
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-
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-
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**Pros**:
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-
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-
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**Cons**:
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-
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-
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**Technical Details**:
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- API: REST | GraphQL | WebSocket
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- Authentication:
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- Rate limits:
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- Data format:
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- SDKs:
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**Documentation**: {link}
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**Community**: {GitHub stars, activity}
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---
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### Option 2: {Name}
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[Same structure]
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---
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### Option 3: {Name}
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[Same structure]
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---
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## Comparison Matrix
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| Criteria | Option 1 | Option 2 | Option 3 | Winner |
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|----------|----------|----------|----------|--------|
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| Cost (MVP) | $X/mo | $Y/mo | $Z/mo | |
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| Features | X | Y | Z | |
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| API Quality | {rating} | {rating} | {rating} | |
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| Documentation | {rating} | {rating} | {rating} | |
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| Community | {rating} | {rating} | {rating} | |
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| Ease of Use | {rating} | {rating} | {rating} | |
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| **Overall** | | | | **{Winner}** |
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---
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## Recommendation
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**Chosen**: {Option X}
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**Rationale**:
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1. {Reason 1}
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2. {Reason 2}
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3. {Reason 3}
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**For MVP**: {Why this is good for MVP}
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**For Production**: {Scalability considerations}
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**Implementation Priority**: {When to implement - MVP/Phase 2/etc}
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---
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## Implementation Notes
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**Setup Steps**:
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1. {Step 1}
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2. {Step 2}
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3. {Step 3}
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**Configuration**:
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```
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{Config example or .env variables needed}
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```
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**Code Example**:
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```{language}
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{Basic usage example if available}
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```
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---
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## Cost Projection
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**MVP** (low volume):
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- Monthly cost: $X
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- Included: {what's covered}
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**Production** (medium volume):
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- Monthly cost: $Y
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- Growth: {how costs scale}
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**At Scale** (high volume):
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- Monthly cost: $Z
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- Optimization: {cost reduction strategies}
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---
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## Risks & Mitigations
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| Risk | Impact | Likelihood | Mitigation |
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|------|--------|-----------|------------|
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| {Risk 1} | High/Med/Low | High/Med/Low | {How to mitigate} |
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| {Risk 2} | High/Med/Low | High/Med/Low | {How to mitigate} |
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---
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## Implementation Checklist
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- [ ] Create account/sign up
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- [ ] Obtain API key/credentials
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- [ ] Test in development environment
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- [ ] Review pricing and set cost alerts
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- [ ] Document integration in architecture
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- [ ] Add credentials to .env.example
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- [ ] Test error handling and rate limits
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---
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## References
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- Official Website: {link}
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- Pricing Page: {link}
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- API Docs: {link}
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- Getting Started: {link}
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- Community: {link}
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- Comparison Articles: {links}
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---
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## Next Steps
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1. ✅ Research complete
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2. Review findings with team (if applicable)
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3. Make final decision on {chosen option}
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4. Update PRD Technical Assumptions with this research
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5. Reference in Architecture document generation
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---
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**Status**: ✅ Research Complete | ⏳ Awaiting Decision | ❌ Needs More Research
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---
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*Fill in this template with findings from ChatGPT/Claude web research.*
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*Save this file when complete.*
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*Architecture generation will reference this research.*
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```
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### Step 5: Present to User
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```
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📋 Research Prompt and Template Generated!
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I've created two files:
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📄 1. Research Prompt
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Location: bmad-backlog/research/RESEARCH-{{topic}}-prompt.md
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This contains a comprehensive research prompt with your project context.
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📄 2. Findings Template
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Location: bmad-backlog/research/RESEARCH-{{topic}}-findings.md
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This is a structured template for documenting research results.
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---
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🔍 Next Steps:
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1. Open: bmad-backlog/research/RESEARCH-{{topic}}-prompt.md
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2. **Copy the entire prompt**
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3. Open ChatGPT (https://chat.openai.com) or Claude (https://claude.ai)
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→ They have web search for current info!
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4. Paste the prompt
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5. Wait for comprehensive research (5-10 minutes)
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6. Copy findings into template:
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bmad-backlog/research/RESEARCH-{{topic}}-findings.md
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7. Save the template file
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8. Come back and run:
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- /bmad:prd (if updating PRD)
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- /bmad:architecture (I'll use your research!)
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---
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Would you like me to show you the research prompt now?
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```
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**If user says yes**:
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- Display the prompt file content
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- User can copy directly
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**If user says no**:
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- "The files are ready when you need them!"
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### Step 6: Store in Pieces
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```
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mcp__Pieces__create_pieces_memory(
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summary_description: "Research prompt for {{topic}}",
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summary: "Generated research prompt for {{topic}}. User will research: {{what to evaluate}}. Purpose: {{why needed for project}}. Findings will inform: {{PRD technical assumptions / Architecture tech stack decisions}}. Template provided for structured documentation.",
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files: [
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"bmad-backlog/research/RESEARCH-{{topic}}-prompt.md",
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"bmad-backlog/research/RESEARCH-{{topic}}-findings.md"
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],
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project: "$(pwd)"
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)
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```
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## Integration with Other Commands
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### Called from `/bmad:prd`
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When PRD generation detects research needs:
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```
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Claude: "I see you need data vendors. Generate research prompt?"
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User: "yes"
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[Runs /bmad:research "data vendors"]
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Claude: "Research prompt generated. Please complete research and return when done."
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[User researches, fills template]
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User: "Research complete"
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Claude: "Great! Continuing PRD with your findings..."
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[Reads RESEARCH-data-vendors-findings.md]
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[Incorporates into PRD Technical Assumptions]
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```
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### Used by `/bmad:architecture`
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Architecture generation automatically checks for research:
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```bash
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ls bmad-backlog/research/RESEARCH-*-findings.md
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```
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If found:
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- Read all findings
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- Use recommendations in tech stack
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- Reference research in Technology Decisions table
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- Include costs from research in cost estimates
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## Voice Feedback
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Voice announces:
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- "Research prompt generated" (when done)
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- "Ready for external research" (reminder)
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## Example Topics
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**Data & APIs**:
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- "data vendors for {domain}"
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- "API marketplaces"
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- "real-time data feeds"
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**Infrastructure**:
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- "hosting platforms for {tech stack}"
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- "CI/CD providers"
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- "monitoring solutions"
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- "CDN providers"
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**Third-Party Services**:
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- "authentication providers"
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- "payment processors"
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- "email services"
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- "SMS providers"
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**AI/ML**:
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- "LLM hosting options"
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- "embedding models"
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- "vector databases"
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## Important Guidelines
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**Always**:
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- ✅ Include project context in prompt
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- ✅ Generate findings template
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- ✅ Guide user to web AI
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- ✅ Store prompts in Pieces
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- ✅ Explain next steps clearly
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**Never**:
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- ❌ Try to research in Claude Code (limited web search)
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- ❌ Hallucinate vendor pricing (use web AI)
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- ❌ Skip generating findings template
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- ❌ Forget project context in prompt
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## Why This Approach
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**Claude Code limitations**:
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- Limited web search
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- Can't browse vendor pricing pages
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- May hallucinate current details
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**ChatGPT/Claude Web strengths**:
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- Actual web search
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- Can browse documentation
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- Current pricing information
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- Community discussions
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- Up-to-date comparisons
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**Best of both worlds**:
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- Claude Code: Generate prompts, manage workflow
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- Web AI: Thorough research with search
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- Result: Informed decisions, documented rationale
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**Cost**: $0 (no API calls, just template generation)
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---
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**This command enables informed technical decisions with documented research!**
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