# Building Blocks for GenAI Evaluation
To move from a promising AI prototype to a scalable tool for social impact, you need more than just sophisticated code—edging toward real-world change requires a deliberate combination of people and process.
This section of the Playbook outlines the two foundational pillars of your evaluation journey: assembling a multidisciplinary team and establishing the technical and conceptual infrastructure to measure success.
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### Building the Team
Success in the development sector depends on breaking down silos. A great GenAI product isn't just "built by engineers" and "checked by researchers"; it is the result of a cross-functional dance.
In this section, we define the specific roles required—from AI Engineers and Data Scientists to Social Scientists and Domain Experts. You’ll find:
* Role Definitions: Who leads which level of evaluation (from model performance to long-term impact).
* Collaboration Best Practices: How to pair technical staff with domain experts early to ensure "accuracy" aligns with "human need."
* Shared Language: Tools for creating a unified vocabulary to avoid the "jargon trap."
Learn more ->
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### Building the Infrastructure
Beyond the people, you need a repeatable system. We define five core building blocks that shift your team from static design to continuous, data-driven improvement.
This section provides a technical and strategic roadmap for:
1. The Foundation: Using formative research and a Theory of Change (TOC) to map how an AI output becomes a social outcome.
2. The User Funnel: Mapping the journey from the first "Hello" to the "North Star" metric, ensuring you don't fall into the "engagement trap."
3. Data Pipelines: Setting up the "Extract, Transform, Load" (ETL) systems necessary to handle complex, unstructured GenAI data.
4. Hypothesis Targeting: A disciplined approach to diagnosing why users drop off or why metrics underperform.
5. Experimentation: Moving from intuition to evidence through A/B testing and rigorous version control.
Learn more ->
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