Generate Solutions (Continuous Discovery Habits)

Goal

To generate multiple potential solutions for identified opportunities using Teresa Torres' Continuous Discovery Habits framework, then systematically evaluate and select the most promising approaches. This framework helps create diverse solutions through structured AI-human collaboration and ideation.

When to Use

After identifying clear opportunities using Create Opportunities
When you have a well-defined target opportunity to solve
Before committing to a single solution approach
When you need to explore different solution directions
After synthesizing user research findings

Input

Primary Input Sources (Choose One)

Option A: Prioritized opportunities from opportunities/ directory
Option B: Direct opportunity input from user (ad-hoc opportunities)
Option C: Mixed approach (file-based + direct input)

Secondary Input Sources

Interview snapshots and synthesis documents
User research findings and behavioral insights
Current solution limitations and pain points
Business context and constraints

Input Requirements

Minimum: At least 1 target opportunity with supporting evidence
Ideal: 3-5 opportunities for comparison and prioritization
Evidence: User quotes, behavioral observations, or research data

Flexible Input Handling

When user provides direct opportunity input:

Validate Input Quality: Ensure opportunity is clearly defined
Request Supporting Evidence: Ask for user quotes or behavioral data
Extract Topic: Analyze opportunity content for semantic filename
File Existence Check: MANDATORY - Check for existing files with pattern solutions-[topic]-v*.md before creating new file
Version Management: MANDATORY - Auto-increment version number based on existing files. Never overwrite existing files
Proceed with Process: Continue with standard solution generation

When user provides mixed input:

Combine Sources: Merge file-based and direct opportunities
Prioritize: Help user select target opportunity from combined list
Standard Process: Follow normal solution generation workflow

Output

Format: Markdown (.md)
Location: solutions/[topic]/
Filename: solutions-[topic]-v[version].md

Semantic Naming Guidelines:

topic: kebab-case topic name extracted from opportunity content (e.g., newsletter-creation, user-onboarding)
version: auto-incrementing version number (v1, v2, v3...)
Example: solutions-newsletter-creation-v1.md

Version Management:

Check existing files with same topic pattern before creating new solutions
Auto-increment version number (v1 → v2 → v3...)
Never overwrite existing solution files
Preserve all solution versions for comparison
No date dependency required

AI Instructions for Solution Generation

MANDATORY PROCESS ENFORCEMENT

CRITICAL: AI MUST NOT SKIP STEP 2 (Individual Ideation)

STEP 2 MANDATORY: Request human to generate individual ideas first
VIOLATION PREVENTION: If human requests solutions without Step 2, AI must:
- STOP immediately
- Explain: "Please generate at least 3 individual ideas first (recommended: 10-15 ideas)"
- Wait for human's ideas before proceeding
- Do NOT generate solutions until Step 2 is completed

Input Validation and Processing

When receiving opportunity data:

Validate Input Quality: Check if opportunities are clearly defined with supporting evidence
Identify Solution Constraints: Note technical, business, and user constraints
Request Additional Context: Ask for more details about user needs and current solutions
Topic Extraction: Analyze opportunity content to extract main topic for semantic filename

For direct opportunity input:

Clarify Context: Ask for user segments, timing, and current solutions
Request Evidence: Ask for specific user quotes or behavioral observations
Validate Scope: Ensure opportunity is appropriately sized (leaf-node level)
Extract Topic: Analyze opportunity content for semantic filename

AI-Human Collaboration Guidelines

MANDATORY WAIT: Never generate solutions before human completes individual ideation
Build on Human Ideas: Use human's ideas as foundation, not starting point
Ask Probing Questions: "What if we looked at this differently?" "How might different user types experience this?"
Expand and Vary: Take human's ideas and suggest variations, improvements, and alternatives
Cross-Pollination: Suggest how ideas could work in different contexts or domains
Maintain Focus: Keep ideation centered on the target opportunity
Encourage Diversity: Push for different types of solutions, not just variations

Quality Standards

Problem Clarity: Each solution clearly addresses the target opportunity
Evidence-Based: Solutions grounded in user research and behavioral insights
Diverse Approaches: Multiple solution types and implementation strategies
Actionable: Solutions that can be realistically implemented

Semantic File Naming Guidelines

Topic Extraction: Analyze opportunity content to identify main topic/theme
Filename Format: Use semantic naming pattern solutions-[topic]-v[version].md
Version Management: Auto-increment version number based on existing files
Folder Organization: Create topic-specific subfolders for better organization
No Date Dependency: Remove all date-based filename requirements

Topic Extraction Process:

Analyze opportunity document for main theme and keywords
Identify the most relevant topic/theme from opportunity content
Convert to kebab-case format (e.g., "Newsletter Creation" → "newsletter-creation")
Ensure topic uniqueness across different solution files
Use topic as primary identifier instead of date

Version Management Process:

MANDATORY STEP 1: Check existing files with pattern solutions-[topic]-v*.md
MANDATORY STEP 2: Find the highest version number for the same topic
MANDATORY STEP 3: Auto-increment version number (v1 → v2 → v3...)
MANDATORY STEP 4: Generate new filename with incremented version
MANDATORY STEP 5: Verify no file with new filename exists before creation
CRITICAL: Never overwrite existing files - always create new version
No manual version tracking required

Smart Topic Detection:

Content Analysis: Extract main themes from opportunity document
Keyword Frequency: Use most frequent relevant keywords as topic
Kebab-Case Format: Convert spaces to hyphens, lowercase (e.g., "User Onboarding" → "user-onboarding")
Uniqueness Check: Ensure topic doesn't conflict with existing files
Fallback: Use generic topic name if extraction fails

Process

0) File Management (MANDATORY PRE-STEP)

Extract topic from opportunity document or direct input
Check existing files with pattern solutions-[topic]-v*.md in target directory
Find highest version number for same topic (e.g., if v1, v2 exist, next is v3)
Generate new filename with incremented version: solutions-[topic]-v[version].md
Verify no file exists with new filename before proceeding
CRITICAL: Never overwrite existing files - always create new version

1. Review Your Target Opportunity

Opportunity Alignment: Ensure both AI and human understand the opportunity and context
Opportunity Validation: Confirm this is an appropriately sized leaf-node opportunity
Context Review: Review supporting evidence and user research findings
Success Criteria: Define what success looks like for this opportunity

For direct input opportunities:

Clarify Details: Ask for user segments, timing, and current solutions
Request Evidence: Ask for specific user quotes or behavioral observations
Validate Scope: Ensure opportunity is appropriately sized

2. Individual Ideation (Human) - MANDATORY

Solo Brainstorming: Human generates initial ideas individually
Overcome Blocks: If stuck, take a break and try again
Seek Inspiration: Look to competitors or similar products for inspiration
Think Broadly: Don't limit to your industry - look at other domains solving similar problems
Quantity Focus: Aim for maximum quantity without judgment
Target: Generate minimum 3 individual ideas before proceeding
- Recommended: 10-15 ideas for better diversity and quality
- Time-constrained: 3-5 ideas acceptable for quick iterations

AI MUST WAIT: Do not proceed to Step 3 until human completes this step

3. AI-Human Collaborative Ideation

Share Initial Ideas: Human presents their ideas to AI
AI Expansion: AI builds on human ideas and generates variations
Cross-Pollination: AI suggests different approaches and categories
Challenge Assumptions: AI asks probing questions to explore new angles
Iterative Refinement: Multiple rounds of collaborative ideation

4. Repeat and Expand (Iterative Ideation)

Multiple Rounds: Continue individual and collaborative ideation sessions
Cross-Pollination: Use each other's ideas to generate more ideas
Don't Skip Individual Work: Continue solo ideation to maximize diversity
Target Quantity: Generate 15-20 ideas for your target opportunity
Embrace Diversity: First ideas aren't always best - seek variety and originality

5. Evaluate and Select Ideas

Initial Filter: Ask "Does this idea address the target opportunity?"
Remove Irrelevant: Filter out ideas that don't address the target opportunity
Collaborative Evaluation: AI and human discuss and evaluate ideas together
Selection Criteria: Focus on how well ideas address the target opportunity
Select Top 3: Choose the 3 most promising ideas through discussion
Document Rationale: Record why these 3 ideas were selected

Process Flow

Continuous Discovery Workflow

Individual Interviews → Create Snapshots → Synthesize Patterns → Create Opportunities → Generate Solutions
     ↓                    ↓                    ↓                    ↓                    ↓
[Raw Data]        [Structured Stories]   [Shared Patterns]    [Problem Statements] [Product Ideas]

Input-Output Relationship

Input: Prioritized opportunities with supporting evidence (from files or direct input)
Process: Topic extraction, AI-human collaborative ideation and evaluation
Output: Top 3 solution approaches with rationale
Next Step: Use selected solutions for further development or testing

Recommended Folder Structure

solutions/
├── newsletter-creation/
│   ├── solutions-newsletter-creation-v1.md
│   └── solutions-newsletter-creation-v2.md
├── user-onboarding/
│   ├── solutions-user-onboarding-v1.md
│   └── solutions-user-onboarding-v2.md
└── payment-flow/
    └── solutions-payment-flow-v1.md

Solution Generation Template

# Solutions for [Opportunity Name]

**Topic:** [Extracted topic name]  
**Version:** [v1, v2, v3...]  
**Target Opportunity:** [Opportunity statement]  
**Source Documents:** [List of opportunities and research documents used]  
**Total Ideas Generated:** [Number] (Target: 15-20)  
**Final Solutions Selected:** 3

---

## Problem Definition

### Target Opportunity Statement
[Clear problem statement from opportunity document]

### Opportunity Context
- **Who:** [User segments affected]
- **When:** [Context and timing of the problem]
- **Current Solutions:** [What exists now and why it fails]
- **Impact:** [How this problem affects users and business]

### Success Criteria
- [Measurable outcome 1]
- [Measurable outcome 2]
- [User behavior change expected]

---

## Ideation Process

### Individual Ideation Sessions
**Human Session 1:** [Date] - [Number] ideas generated
**Human Session 2:** [Date] - [Number] ideas generated
**Human Session 3:** [Date] - [Number] ideas generated

### AI-Human Collaborative Sessions
**Session 1:** [Date] - [Key insights and new ideas generated]
**Session 2:** [Date] - [Key insights and new ideas generated]
**Session 3:** [Date] - [Key insights and new ideas generated]

### Total Ideas Generated: [Number]

---

## Idea Evaluation

### Initial Filter
**Ideas that address target opportunity:** [Number]
**Ideas removed (don't address opportunity):** [Number]

### Collaborative Evaluation Process
**Evaluation Criteria:**
- How well does the idea address the target opportunity?
- Is the idea feasible given our constraints?
- Does the idea represent a different approach from others?
- Is the idea grounded in user research?

### Evaluation Results
| Idea | Addresses Opportunity | Feasibility | Uniqueness | Evidence-Based | Notes |
|------|----------------------|-------------|------------|----------------|-------|
| Idea 1 | [Yes/No] | [High/Med/Low] | [High/Med/Low] | [Yes/No] | [Comments] |
| Idea 2 | [Yes/No] | [High/Med/Low] | [High/Med/Low] | [Yes/No] | [Comments] |
| Idea 3 | [Yes/No] | [High/Med/Low] | [High/Med/Low] | [Yes/No] | [Comments] |

---

## Selected Solutions

### Solution 1: [Solution Name]
**Type:** [Feature/Process/Interface/Business Model]  
**Approach:** [How this solution works]  

**Description:**
[Detailed explanation of the solution approach]

**Key Features:**
- [Feature 1]
- [Feature 2]
- [Feature 3]

**User Experience:**
[How users will interact with this solution]

**Why Selected:**
[Rationale for why this solution was chosen]

**Implementation Considerations:**
- **Feasibility:** [Technical and resource considerations]
- **User Value:** [How well this solves the problem]
- **Business Value:** [Alignment with business goals]
- **Risks:** [Potential challenges and mitigation strategies]

---

### Solution 2: [Solution Name]
[Repeat structure for each solution]

---

### Solution 3: [Solution Name]
[Repeat structure for each solution]

---

## Next Steps
- [ ] [Next development action 1]
- [ ] [Next development action 2]
- [ ] [Research action 1]
- [ ] [Research action 2]

Process Validation Checkpoints (RECOMMENDED)

Before proceeding to each step, AI should verify:

Step 1: Target opportunity clearly defined with success criteria
Step 2: Human has generated minimum 3 individual ideas (MANDATORY)
Step 3: Human's ideas have been shared and reviewed
Step 4: Multiple rounds of collaborative ideation completed (15-20 total ideas)
Step 5: Collaborative evaluation and selection completed

Note: These checkpoints are recommended for quality assurance but not mandatory for basic usage.

Common Antipatterns to Avoid

1. Not Including Diverse Perspectives

Problem: Limiting ideation to single perspective or approach
Solution: AI acts as diverse thinking partner, challenging assumptions
Why: Diversity leads to more diverse ideas
Practice: AI asks probing questions and suggests alternative approaches

2. Generating Too Many Variations of the Same Idea

Problem: Focusing on variations rather than different approaches
Solution: AI helps identify categorically different ideas
Why: Diversity matters more than quantity when selecting final 3
Practice: AI suggests different solution categories and approaches

3. Limiting Ideation to One Session

Problem: Trying to generate all ideas in a single interaction
Solution: Allow ideas to incubate over multiple sessions
Why: Brain's problem-incubation capability is powerful
Practice: Schedule multiple ideation sessions with breaks between

4. Selecting Ideas That Don't Address the Target Opportunity

Problem: Getting distracted by interesting but irrelevant ideas
Solution: AI helps maintain focus on target opportunity
Why: Maintains focus on strategic decisions made during opportunity selection
Practice: AI consistently asks "Does this idea address the target opportunity?"

AI-Human Collaboration Techniques

1. AI Expansion Techniques

Build on Ideas: Take human's idea and suggest variations or improvements
Cross-Category Thinking: Suggest how idea could work in different contexts
Constraint Exploration: Explore how idea could work with different constraints
Analogous Inspiration: Suggest similar solutions from other domains

2. AI Challenge Techniques

Assumption Testing: Question underlying assumptions in ideas
Perspective Shifting: Ask "What if we looked at this differently?"
Constraint Addition: Explore "What if we had to do this with [constraint]?"
User Perspective: Ask "How would different user types experience this?"

3. AI Synthesis Techniques

Pattern Recognition: Identify common themes across ideas
Category Creation: Group similar ideas and suggest missing categories
Gap Analysis: Identify areas where no ideas have been generated
Combination Suggestions: Propose combining elements from different ideas

Solution Categories

1. Feature-Based Solutions

New Features: Adding new capabilities to existing products
Feature Improvements: Enhancing current features
Feature Removal: Simplifying by removing complexity

2. Process Solutions

Workflow Changes: Modifying how users complete tasks
Automation: Reducing manual steps
Integration: Connecting different tools or systems

3. Interface Solutions

UI/UX Improvements: Better visual design and interaction
Accessibility: Making solutions usable by more people
Mobile/Responsive: Adapting for different devices

4. Business Model Solutions

Pricing Changes: Adjusting cost structure
Distribution: Changing how users access the solution
Partnerships: Working with others to solve problems

Quality Indicators

Strong Solutions

Clear Problem Focus: Directly addresses the target opportunity
User-Centered: Based on actual user needs and behaviors
Feasible: Realistic given constraints and resources
Diverse: Represents different approaches to solving the problem
Evidence-Based: Grounded in user research and insights

Weak Solutions

Vague Description: Unclear how the solution works
Assumption-Based: Not grounded in user research
Unrealistic: Beyond current capabilities or resources
Too Similar: Variations of the same basic approach
Off-Target: Doesn't address the target opportunity

Error Handling

Insufficient Data

Unclear Opportunities: Request clearer problem definition before solution generation
Missing Context: Ask for additional user research or behavioral insights
Weak Evidence: Highlight areas needing stronger user validation

Quality Issues

Solution-First Thinking: Help reframe as problem-focused solutions
Assumption-Based Solutions: Push for user research validation
Lack of Diversity: Encourage different types of solutions
Off-Target Ideas: Redirect focus to target opportunity

File Naming Issues

Topic Extraction Failure: Ensure clear topic identification from opportunity content
Version Conflicts: Always check existing files before creating new solutions
Incorrect Format: Use kebab-case for topic, v[number] for version
Missing Topic: Extract main theme from opportunity document for filename
Overwriting Prevention: Never overwrite existing solution files, always increment version
File Existence Check Failure: MANDATORY - Always verify file existence before creation
Version Detection Error: If version detection fails, start with v1 and add warning note

Best Practices

Do's

Generate 15-20 ideas before selecting top 3
Use AI as a thinking partner, not just an executor
Allow ideas to incubate over multiple sessions
Focus on ideas that address the target opportunity
Collaborate iteratively between human and AI
Build on each other's ideas
Challenge assumptions and explore different angles

Don'ts

Don't limit ideation to one session
Don't generate only variations of the same idea
Don't include ideas that don't address the target opportunity
Don't skip the individual ideation step
Don't fall in love with your first solution
Don't ignore AI's suggestions and challenges
Don't assume you know what users want

Quality Assurance Checklist

Input Validation

Clear target opportunity statement with supporting evidence
Opportunity context and problem definition complete
Current solutions and their limitations documented

Ideation Quality

15-20 ideas generated through multiple sessions
AI-human collaboration used effectively
Ideas represent different approaches, not just variations
All ideas address the target opportunity

Selection Quality

Collaborative evaluation process completed
Top 3 ideas selected based on target opportunity alignment
Selection rationale documented
Selected ideas are feasible and actionable

Process Completion

All solutions evaluated against criteria
Implementation considerations documented
Next steps are clear and actionable

File Naming Validation

MANDATORY: Extracted clear topic from opportunity content before creating filename
Filename uses semantic naming format: solutions-[topic]-v[version].md
Version number is correctly auto-incremented for same-topic solutions
Topic name is descriptive and kebab-case formatted
No overwriting of existing solution files
Topic extraction completed through content analysis before solution creation

AI Implementation Checklist

Before Creating Any Solution File:

Topic Extraction ✅
- Analyze opportunity document for main theme
- Extract kebab-case topic name (e.g., "newsletter-creation")
- Verify topic is descriptive and unique
File Existence Check ✅
- Search for existing files: solutions-[topic]-v*.md
- List all found files with their version numbers
- Identify highest version number
Version Management ✅
- Calculate next version number (highest + 1)
- Generate new filename: solutions-[topic]-v[version].md
- Verify new filename doesn't already exist
File Creation ✅
- Create file with new filename only
- Never overwrite existing files
- Add version number to document header

Error Handling:

If topic extraction fails: Use generic topic name and add warning
If version detection fails: Start with v1 and add warning note
If file already exists: Increment version and try again
If multiple topics found: Use most relevant one and document choice

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