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Prototyping Gen AI Applications: A Practical Guide to Doing It Right
- Generative AI (Gen AI) applications require a balanced approach between user needs, business objectives, and technical capabilities, with the need for rapid prototyping due to the unpredictable nature of outputs.
- Marty Cagan outlines four types of prototypes for Gen AI projects: Feasibility, User, Live-Data, and Hybrid Prototypes, crucial for testing integrations, usability, and real data.
- Differentiating between proof of concept (POC) and prototypes, prototypes focus on user functionality while POC validates technical feasibility.
- The DVF framework by IDEO organizes prototypes around Desirability, Viability, and Feasibility, critical for focusing limited resources on key unknowns.
- Enterprise contexts introduce additional considerations like data governance, security, and compliance, which should be incorporated early into prototyping efforts.
- Viability prototyping assesses sustainability and alignment with business objectives, while feasibility prototyping validates technical capabilities and infrastructure readiness.
- Five key principles for effective Gen AI prototyping include starting simple, adding complexity incrementally, testing edge cases early, matching prototype fidelity to objectives, and planning for continuous iteration.
- Prototyping is an iterative process essential for maintaining alignment with evolving user requirements and technical constraints in successful Gen AI application development.
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