Implementation Timeline
flowchart TB
subgraph TopRow[" "]
direction LR
subgraph Phase1["📋 Phase 1<br/>Research Planning"]
direction TB
S1["1. Define Scope"]
S2["2. Set Objectives"]
S3["3. Prepare Documentation"]
S1 --> S2 --> S3
end
subgraph Phase3["🌳 Phase 3<br/>Tree Construction"]
direction TB
S7["7. Extract Core Problem"]
S8["8. Map Root Causes"]
S9["9. Identify Effects"]
S7 --> S8 --> S9
end
end
subgraph BottomRow[" "]
direction LR
subgraph Phase2["🤖 Phase 2<br/>AI-Assisted Research"]
direction TB
S4["4. Customize MCP"]
S5["5. Execute Research"]
S6["6. Verify Quality"]
S4 --> S5 --> S6
end
subgraph Phase4["💬 Phase 4<br/>Stakeholder Prep"]
direction TB
S10["10. Convert to Questions"]
S11["11. Plan Validation"]
S10 --> S11
end
end
S3 -.-> S4
S6 -.-> S7
S9 -.-> S10
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style BottomRow fill:transparent,stroke:transparent
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style Phase2 fill:#FEF3C7,stroke:#F59E0B,stroke-width:2px,color:#1F2937
style Phase3 fill:#FEF3C7,stroke:#F59E0B,stroke-width:2px,color:#1F2937
style Phase4 fill:#FEF3C7,stroke:#F59E0B,stroke-width:2px,color:#1F2937
style S1 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S2 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S3 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S4 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S5 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S6 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S7 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S8 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S9 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S10 fill:#D9F99D,stroke:#72B043,color:#2A2A2A
style S11 fill:#007F4E,stroke:#00b369,stroke-width:2px,color:#fff,font-weight:bold
Phase 1: Research Planning
- Step 1: Define Your Problem Scope
-
- Write a clear 1-2 sentence problem statement
- Specify affected population and geographic area
- List what you already know
- Step 2: Set Research Objectives
-
- Causes and effects you need to understand
- Interventions the research should help you design
- Stakeholders you'll eventually need to engage
- Step 3: Prepare Documentation System
-
- Create a folder for sources
- Start from the Problem Tree template
- Track evidence (E) versus assumptions (A)
Example Problem Statement
Young adults aged 18-25 in rural Nyanza region, Kenya, have limited access to stable employment.
Phase 2: AI-Assisted Research
- Step 4: Customize Your MCP Prompt
-
- Copy the MCP template
- Replace placeholders with your context
- Step 5: Execute and Review AI Research
-
- Run the prompt, save outputs immediately
- Scan for errors or red flags
- Step 6: Quality Verification
-
- Check credibility of 3-5 sources
- Verify key statistics against original sources
- Flag anything that looks like assumption
Don't Skip Verification
AI can generate plausible but incorrect citations.
Phase 3: Problem Tree Construction
- Step 7: Extract and Organize Core Problem
-
- Refine the problem statement
- Specify who, what, and where
- Remove any causes or solutions
- Step 8: Map Root Causes by Levels
-
- Level 1: direct causes
- Level 2: underlying causes
- Level 3: structural causes
- Tag each cause (E) evidence-based or (A) assumption
- Step 9: Identify Effects by Time Horizon
-
- Immediate, medium-term, and long-term effects
- Individual, family, community, and system levels
- Tag each effect (E) or (A)
Root Cause Levels
• Skills-labor market mismatch (E)
- Educational curricula not aligned with market needs (E)
- Limited access to practical/vocational training (E)
- Rapid economic transition outpacing skill development (A)
Milestone: Your Problem Tree is Complete!
Causes, problem, and effects, all tagged and ready for validation.
Phase 4: Stakeholder Preparation
- Step 10: Convert Assumptions to Questions
-
- Turn each (A) item into an open-ended question
- Avoid leading questions
- Aim for 8-10 questions
- Step 11: Plan Validation Approach
-
- Identify who can answer your questions
- Decide how you'll reach them: focus groups, interviews, meetings
- Update your tree based on what you learn
Bad Leading Question
"Isn't training the real problem here?"
Good Open-Ended Question
"What keeps young people from finding good work?"
Common Questions
- Generic results?
- Add more geography and population detail.
- Deep enough?
- Stop once you reach something actionable.
- Skip Phase 2?
- MCP is optional but saves hours.