> For the complete documentation index, see [llms.txt](https://eval.playbook.org.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://eval.playbook.org.ai/getting-started/building-blocks-for-genai-evaluation.md). # 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. *** ### 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 -> *** ### 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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