Clarification Protocol

Clarification Protocol

Generates focused, challenging questions to extract missing context and clarify vague requirements before routing to specialist agents.

When to Use

• After request-analyzer identifies vague terms or missing context
• When requirements are ambiguous and could lead to wrong solutions
• Before delegating to cto-architect or strategic-cto-mentor
• When buzzwords need to be translated into specific requirements

Core Principles

1. Maximum 2-3 Questions Per Round

Users lose patience with long questionnaires. Prioritize ruthlessly:

• Ask only what's blocking progress
• Combine related questions
• Defer nice-to-have information

2. Challenge Mode, Not Interview Mode

Don't just ask—challenge assumptions:

• Bad: "What scale do you need?"
• Good: "You mentioned 'scalable'—are we designing for 10K users or 10M? That changes the architecture significantly."

3. Provide Example Answers

Help users understand what you're looking for:

• Bad: "What's your timeline?"
• Good: "What's your timeline? For context, a robust MVP typically takes 8-12 weeks with a team of 4."

Question Generation Framework

Step 1: Prioritize Missing Information

Rank by impact on routing and design:

Ask P0 first, then P1. Defer P2/P3.

Step 2: Select Question Type

Step 3: Frame as Challenge

Transform neutral questions into challenging ones:

Output Format

## Clarification Needed

Before I route this to the appropriate specialist, I need to understand a few things:

### Challenge 1: [Vague Term/Concept]
[Direct challenge statement]

**Question**: [Specific question]

**Why this matters**: [Impact on design/routing]

**Example answers**:
- Option A: [example] → leads to [approach]
- Option B: [example] → leads to [different approach]

### Challenge 2: [Missing Context]
[...]

---

Once you clarify these points, I'll route to [suggested agent] with a clear brief.

Question Templates by Category

See question-templates.md for comprehensive templates.

See challenge-patterns.md for examples of turning neutral questions into challenges.

Examples

Example 1: AI Feature Request

User Request: "We want to add AI capabilities to improve customer experience"

Clarification Output:

## Clarification Needed

Before I route this to cto-architect, I need to understand what "AI capabilities" means for your use case:

### Challenge 1: Vague AI Requirement
"AI capabilities" could mean anything from a chatbot to predictive analytics. I don't want to design the wrong solution.

**Question**: What specific customer problem are you trying to solve with AI?

**Why this matters**: A recommendation engine has completely different architecture than a support chatbot.

**Example answers**:
- "Customers can't find products" → Recommendation engine / search improvement
- "Support tickets are overwhelming" → AI-assisted ticket routing or chatbot
- "Churn is high" → Predictive analytics for at-risk customers

### Challenge 2: Missing Scale
**Question**: How many customers are we talking about? And how many interactions per day would this AI feature handle?

**Why this matters**: 1,000 daily interactions vs 1,000,000 changes whether we use a simple API call or need dedicated ML infrastructure.

---

Once you clarify these points, I'll route to cto-architect with a clear brief for the appropriate AI architecture.

Example 2: Scaling Concerns

User Request: "Our app needs to scale better"

Clarification Output:

## Clarification Needed

"Scale better" is a symptom—I need to understand the disease before prescribing treatment.

### Challenge 1: What's Actually Broken?
**Question**: What specific scaling issue are you experiencing? Slow page loads? Database timeouts? High infrastructure costs? All of the above?

**Why this matters**: Scaling a slow database is different from scaling a CPU-bound API.

**Example answers**:
- "Page loads take 5+ seconds at peak" → Likely caching/CDN solution
- "Database queries timing out" → Database scaling or query optimization
- "AWS bill doubled last month" → Cost optimization vs actual scaling

### Challenge 2: Current vs Target Scale
**Question**: What's your current traffic, and what do you need to support? Be specific: requests per second, concurrent users, data volume.

**Why this matters**: "Scale" at 10K users looks very different from "scale" at 10M users. I don't want to over-engineer.

---

Once I understand the specific bottleneck, I'll route to the appropriate specialist.

Anti-Patterns to Avoid

1. The Interrogation

Bad: Asking 10 questions at once Good: Max 2-3 targeted questions

2. The Open-Ended Trap

Bad: "Tell me more about your requirements" Good: "Is this for internal users (hundreds) or external customers (thousands+)?"

3. The Assumption Question

Bad: "What microservices architecture do you want?" (assumes microservices) Good: "What's your current architecture, and what's driving the need to change?"

4. The Jargon Barrier

Bad: "What's your CAP theorem preference for the distributed system?" Good: "If the system goes offline briefly, should it prioritize consistency (everyone sees the same data) or availability (the system stays up)?"

References

• Question Templates - Ready-to-use question templates by category
• Challenge Patterns - Examples of effective challenge framing

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