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description

description
Generate research prompts for technical decisions

BMAD Research - Generate Research Prompts

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.

Purpose

Generate structured research prompts that users can copy to ChatGPT/Claude web to research:

API vendors and data sources
Authentication providers
Hosting platforms
Payment processors
Third-party integrations
Technology stack options

Results are documented in structured templates and referenced during architecture generation.

When to Use

During BMAD workflow:

After PRD mentions external APIs/vendors
Before architecture generation
When technical decisions need research

Standalone:

Evaluating vendor options
Comparing technologies
Cost analysis
Technical due diligence

Process

Step 1: Identify Research Topic

If user provided topic:

# User ran: /bmad:research "data vendors for precious metals"

Topic = "data vendors for precious metals"

If no topic:

Ask: "What do you need to research?"

Show common topics:

Common research topics:
1. Data vendors/APIs
2. Hosting platforms (Railway, Vercel, GCP, etc.)
3. Authentication providers (Clerk, Auth0, custom, etc.)
4. Payment processors (Stripe, PayPal, etc.)
5. AI/ML options (OpenAI, Anthropic, self-hosted)
6. Database options
7. Other (specify)

Topic:

Step 2: Gather Context from PRD

If PRD exists:

Read bmad-backlog/prd/prd.md

Extract relevant context:

What features need this research?
What are the constraints? (budget, performance)
Any technical preferences mentioned?

If no PRD:

Use topic only
Generate generic research prompt
Note: "Research will be more focused with a PRD"

Step 3: Generate Research Prompt

Create comprehensive prompt for web AI.

Topic slug: Convert topic to filename-safe string

topic_slug = topic.lower().replace(' ', '-').replace('/', '-')
# "data vendors for precious metals" → "data-vendors-for-precious-metals"

Save to: bmad-backlog/research/RESEARCH-{topic_slug}-prompt.md

Prompt content:

# Research Prompt: {Topic}

**COPY THIS ENTIRE PROMPT** and paste into ChatGPT (GPT-4) or Claude (web).
They have web search and can provide current, comprehensive research.

---

## Research Request

**Project**: {{project name from PRD or "New Project"}}

**Research Topic**: {{topic}}

**Context**:
{{Extract from PRD:
- What features need this
- Performance requirements
- Budget constraints
- Technical preferences}}

---

## What I Need

Please research and provide:

### 1. Overview
- What options exist for {{topic}}?
- What are the top 5-7 solutions/vendors/APIs?
- Current market leaders?

### 2. Comparison Table

Create a detailed comparison table:

| Option | Pricing | Key Features | Pros | Cons | Best For |
|--------|---------|--------------|------|------|----------|
| Option 1 | | | | | |
| Option 2 | | | | | |
| Option 3 | | | | | |

### 3. Technical Details

For each option, provide:
- **API Documentation**: Official docs link
- **Authentication**: API key, OAuth, etc.
- **Rate Limits**: Requests per minute/hour
- **Data Format**: JSON, XML, GraphQL, etc.
- **SDKs**: Python, Node.js, etc. with links
- **Code Examples**: If available
- **Community**: GitHub stars, Stack Overflow activity

### 4. Integration Complexity

For each option:
- **Estimated Setup Time**: Hours/days
- **Dependencies**: What else is needed
- **Learning Curve**: Easy/Medium/Hard
- **Documentation Quality**: Excellent/Good/Poor
- **Community Support**: Active/Moderate/Limited

### 5. Recommendations

Based on my project requirements:
{{List key requirements}}

Which option would you recommend and why?

Provide recommendation for:
- **MVP**: Best for getting started quickly
- **Production**: Best for long-term reliability
- **Budget**: Most cost-effective option

### 6. Cost Analysis

For each option, provide:

**Free Tier**:
- What's included
- Limitations
- Good for MVP? (yes/no)

**Paid Tiers**:
- Tier names and pricing
- What each tier includes
- Rate limit increases

**Estimated Monthly Cost**:
- MVP (low volume): $X-Y
- Production (medium volume): $X-Y
- Scale (high volume): $X-Y

### 7. Risks & Considerations

For each option:
- **Vendor Lock-in**: How easy to migrate away?
- **Data Quality**: Accuracy, freshness, reliability
- **Compliance**: Regional restrictions, data governance
- **Uptime/SLA**: Published SLAs, historical uptime
- **Support**: Response times, support channels

### 8. Source Links

Provide links to:
- Official website
- Pricing page
- API documentation
- Getting started guide
- Community forums/Discord
- Comparison articles/reviews
- GitHub repositories (if applicable)

---

## Deliverable Format

Please structure your response to match the sections above for easy copy/paste into my findings template.

Thank you!

Write this to file: bmad-backlog/research/RESEARCH-{topic_slug}-prompt.md

Step 4: Generate Findings Template

Create structured template for documenting research.

Save to: bmad-backlog/research/RESEARCH-{topic_slug}-findings.md

Template content:

# Research Findings: {Topic}

**Date**: {current date}
**Researcher**: {user name or TBD}
**Status**: Draft

---

## Research Summary

**Question**: {what was researched}

**Recommendation**: {chosen option and why}

**Confidence**: High | Medium | Low

---

## Options Evaluated

### Option 1: {Name}

**Overview**:

**Pricing**:
- Free tier:
- Paid tiers:
- Estimated cost for MVP: $X/month
- Estimated cost for Production: $Y/month

**Features**:
-
-

**Pros**:
-
-

**Cons**:
-
-

**Technical Details**:
- API: REST | GraphQL | WebSocket
- Authentication:
- Rate limits:
- Data format:
- SDKs:

**Documentation**: {link}

**Community**: {GitHub stars, activity}

---

### Option 2: {Name}

[Same structure]

---

### Option 3: {Name}

[Same structure]

---

## Comparison Matrix

| Criteria | Option 1 | Option 2 | Option 3 | Winner |
|----------|----------|----------|----------|--------|
| Cost (MVP) | $X/mo | $Y/mo | $Z/mo | |
| Features | X | Y | Z | |
| API Quality | {rating} | {rating} | {rating} | |
| Documentation | {rating} | {rating} | {rating} | |
| Community | {rating} | {rating} | {rating} | |
| Ease of Use | {rating} | {rating} | {rating} | |
| **Overall** | | | | **{Winner}** |

---

## Recommendation

**Chosen**: {Option X}

**Rationale**:
1. {Reason 1}
2. {Reason 2}
3. {Reason 3}

**For MVP**: {Why this is good for MVP}

**For Production**: {Scalability considerations}

**Implementation Priority**: {When to implement - MVP/Phase 2/etc}

---

## Implementation Notes

**Setup Steps**:
1. {Step 1}
2. {Step 2}
3. {Step 3}

**Configuration**:

{Config example or .env variables needed}


**Code Example**:
```{language}
{Basic usage example if available}

Cost Projection

MVP (low volume):

Monthly cost: $X
Included: {what's covered}

Production (medium volume):

Monthly cost: $Y
Growth: {how costs scale}

At Scale (high volume):

Monthly cost: $Z
Optimization: {cost reduction strategies}

Risks & Mitigations

Risk	Impact	Likelihood	Mitigation
{Risk 1}	High/Med/Low	High/Med/Low	{How to mitigate}
{Risk 2}	High/Med/Low	High/Med/Low	{How to mitigate}

Implementation Checklist

Create account/sign up
Obtain API key/credentials
Test in development environment
Review pricing and set cost alerts
Document integration in architecture
Add credentials to .env.example
Test error handling and rate limits

References

Official Website: {link}
Pricing Page: {link}
API Docs: {link}
Getting Started: {link}
Community: {link}
Comparison Articles: {links}

Next Steps

✅ Research complete
Review findings with team (if applicable)
Make final decision on {chosen option}
Update PRD Technical Assumptions with this research
Reference in Architecture document generation

Status: ✅ Research Complete | ⏳ Awaiting Decision | ❌ Needs More Research

Fill in this template with findings from ChatGPT/Claude web research. Save this file when complete. Architecture generation will reference this research.


### Step 5: Present to User

📋 Research Prompt and Template Generated!

I've created two files:

📄 1. Research Prompt Location: bmad-backlog/research/RESEARCH-{{topic}}-prompt.md

This contains a comprehensive research prompt with your project context.

📄 2. Findings Template Location: bmad-backlog/research/RESEARCH-{{topic}}-findings.md

This is a structured template for documenting research results.

🔍 Next Steps:

Open: bmad-backlog/research/RESEARCH-{{topic}}-prompt.md
Copy the entire prompt
Open ChatGPT (https://chat.openai.com) or Claude (https://claude.ai) → They have web search for current info!
Paste the prompt
Wait for comprehensive research (5-10 minutes)
Copy findings into template: bmad-backlog/research/RESEARCH-{{topic}}-findings.md
Save the template file
Come back and run:
- /bmad:prd (if updating PRD)
- /bmad:architecture (I'll use your research!)

Would you like me to show you the research prompt now?


**If user says yes**:
- Display the prompt file content
- User can copy directly

**If user says no**:
- "The files are ready when you need them!"

### Step 6: Store in Pieces

mcp__Pieces__create_pieces_memory( summary_description: "Research prompt for {{topic}}", 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.", files: [ "bmad-backlog/research/RESEARCH-{{topic}}-prompt.md", "bmad-backlog/research/RESEARCH-{{topic}}-findings.md" ], project: "$(pwd)" )


## Integration with Other Commands

### Called from `/bmad:prd`

When PRD generation detects research needs:

Claude: "I see you need data vendors. Generate research prompt?" User: "yes"

[Runs /bmad:research "data vendors"]

Claude: "Research prompt generated. Please complete research and return when done."

[User researches, fills template]

User: "Research complete"

Claude: "Great! Continuing PRD with your findings..." [Reads RESEARCH-data-vendors-findings.md] [Incorporates into PRD Technical Assumptions]


### Used by `/bmad:architecture`

Architecture generation automatically checks for research:
```bash
ls bmad-backlog/research/RESEARCH-*-findings.md

If found:

Read all findings
Use recommendations in tech stack
Reference research in Technology Decisions table
Include costs from research in cost estimates

Voice Feedback

Voice announces:

"Research prompt generated" (when done)
"Ready for external research" (reminder)

Example Topics

Data & APIs:

"data vendors for {domain}"
"API marketplaces"
"real-time data feeds"

Infrastructure:

"hosting platforms for {tech stack}"
"CI/CD providers"
"monitoring solutions"
"CDN providers"

Third-Party Services:

"authentication providers"
"payment processors"
"email services"
"SMS providers"

AI/ML:

"LLM hosting options"
"embedding models"
"vector databases"

Important Guidelines

Always:

✅ Include project context in prompt
✅ Generate findings template
✅ Guide user to web AI
✅ Store prompts in Pieces
✅ Explain next steps clearly

Never:

❌ Try to research in Claude Code (limited web search)
❌ Hallucinate vendor pricing (use web AI)
❌ Skip generating findings template
❌ Forget project context in prompt

Why This Approach

Claude Code limitations:

Limited web search
Can't browse vendor pricing pages
May hallucinate current details

ChatGPT/Claude Web strengths:

Actual web search
Can browse documentation
Current pricing information
Community discussions
Up-to-date comparisons

Best of both worlds:

Claude Code: Generate prompts, manage workflow
Web AI: Thorough research with search
Result: Informed decisions, documented rationale

Cost: $0 (no API calls, just template generation)

This command enables informed technical decisions with documented research!

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