From the course: AI Project Coordination for Lead Data Scientists
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Project scoping and stakeholder alignment
From the course: AI Project Coordination for Lead Data Scientists
Project scoping and stakeholder alignment
- Scoping an AI project used to mean defining inputs, outputs, and maybe a data pipeline. But if you're working with LLMs or agentic workflows, you are not building a model. You're curating behavior across a fluid architecture. APIs, prompts, memory and human feedback loops, which means a person responding back. It means scoping isn't a checklist, it's an alignment ritual. At this level, you are not just parsing requirements, you're decoding organizational ambiguity. And if you don't front load clarity, your whole build will spiral into prompt bloat, tool chaos and executive disappointment. Let's break this into three things you need to land cleanly. First, define the problem like it's a product, not a paper. Papers don't really get much done, and products are more practical and they have business value. AI projects get stuck when they start with vibes and vague intent. We want a smart AI assistant or a GPT for sales.…
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